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초기화---이 노트북에서는 데이터 폴더에 추론 디렉토리를 추가하게끔 저장소 구조를 갱신합니다.```/lookout-equipment-demo\|+-- data/\| \|\| +-- inference/\| \| \|\| \| \|-- input/\| \| \|\| \| \-- output/\| \|\| +-- labelled-data/\| \| \-- labels.csv\| \|\| \-- training-data/\| \-- expander/\| \|-- subsystem-01\| \| \-- subsystem-01.csv\| \|\| \|-- subsystem-02\| \| \-- subsystem-02.csv\| \|\| \|-- ...\| \|\| \-- subsystem-24\| \-- subsystem-24.csv\|+-- dataset/\| \|-- labels.csv\| \|-- tags_description.csv\| \|-- timeranges.txt\| \-- timeseries.zip\|+-- notebooks/\| \|-- 1_data_preparation.ipynb\| \|-- 2_dataset_creation.ipynb\| \|-- 3_model_training.ipynb\| \|-- 4_model_evaluation.ipynb\| \-- 5_inference_scheduling.ipynb <<< 본 노트북 <<<\|+-- utils/ \|-- lookout_equipment_utils.py \-- lookoutequipment.json``` 임포트	%%sh pip -q install --upgrade pip pip -q install --upgrade awscli boto3 sagemaker aws configure add-model --service-model file://../utils/lookoutequipment.json --service-name lookoutequipment from IPython.core.display import HTML HTML("<script>Jupyter.notebook.kernel.restart()</script>") import boto3 import datetime import os import pandas as pd import pprint import pyarrow as pa import pyarrow.parquet as pq import sagemaker import s3fs import sys import time import uuid import warnings # Lookout for Equipment API 호출 관리를 위한 Helper 함수 sys.path.append('../utils') import lookout_equipment_utils as lookout	_____no_output_____	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
파라미터	warnings.filterwarnings('ignore') DATA = os.path.join('..', 'data') RAW_DATA = os.path.join('..', 'dataset') INFER_DATA = os.path.join(DATA, 'inference') os.makedirs(os.path.join(INFER_DATA, 'input'), exist_ok=True) os.makedirs(os.path.join(INFER_DATA, 'output'), exist_ok=True) ROLE_ARN = sagemaker.get_execution_role() REGION_NAME = boto3.session.Session().region_name	_____no_output_____	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
추론 스케줄러 생성하기---콘솔의 모델 세부 정보 부분으로 이동하면 추론 스케줄이 아직 없음을 확인할 수 있습니다.![Schedule Starting point](../assets/schedule_start.png) 스케줄러 설정새로운 추론 스케줄을 만들어 보겠습니다. 파라미터 일부는 필수 입력이지만 파라미터 다수는 유연하게 추가 설정할 수 있습니다. 파라미터* 추론을 위해 데이터를 업로드할 빈도로 `DATA_UPLOAD_FREQUENCY`를 설정합니다. 허용되는 값은`PT5M`,`PT10M`,`PT15M`,`PT30M`과`PT1H`입니다. * 이것은 추론 스케줄러가 실행되는 빈도와 데이터가 소스 버킷에 업로드되는 빈도입니다. * 참고 : *업로드 빈도는 훈련 때 선택한 샘플링 비율과 호환되어야합니다.* 예를 들어 모델을 30분 간격의 리샘플링으로 훈련시킨 경우 5분은 가능하지 않습니다. 추론 시 파라미터로 PT30M 또는 PT1H를 선택해야합니다.* 추론 데이터의 S3 버킷으로 `INFERENCE_DATA_SOURCE_BUCKET`를 설정합니다.* 추론 데이터의 S3 접두사로 `INFERENCE_DATA_SOURCE_PREFIX`를 설정합니다.* 추론 결과를 원하는 S3 버킷으로 `INFERENCE_DATA_OUTPUT_BUCKET`를 설정합니다.* 추론 결과를 원하는 S3 접두사로 `INFERENCE_DATA_OUTPUT_PREFIX`를 설정합니다.* 추론할 데이터를 읽고 추론 출력을 쓸 때 사용할 역할로 `ROLE_ARN_FOR_INFERENCE`를 설정합니다.	# 생성하려는 추론 스케줄러의 이름 INFERENCE_SCHEDULER_NAME = 'lookout-demo-model-v1-scheduler' # 본 추론 스케줄러를 생성할 모델의 이름 MODEL_NAME_FOR_CREATING_INFERENCE_SCHEDULER = 'lookout-demo-model-v1' # 필수 입력 파라미터 INFERENCE_DATA_SOURCE_BUCKET = BUCKET INFERENCE_DATA_SOURCE_PREFIX = f'{PREFIX}/input/' INFERENCE_DATA_OUTPUT_BUCKET = BUCKET INFERENCE_DATA_OUTPUT_PREFIX = f'{PREFIX}/output/' ROLE_ARN_FOR_INFERENCE = ROLE_ARN DATA_UPLOAD_FREQUENCY = 'PT5M'	_____no_output_____	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
생략 가능한 파라미터* 데이터 업로드하는데 지연이 예상되는 시간(분)으로 `DATA_DELAY_OFFSET_IN_MINUTES`를 설정합니다. 즉, 데이터 업로드하는 시간에 대한 버퍼입니다.* ``INPUT_TIMEZONE_OFFSET``을 설정합니다. 허용되는 값은 +00:00, +00:30, -01:00, ... +11:30, +12:00, -00:00, -00:30, -01:00, ... -11:30, -12:00입니다.* `TIMESTAMP_FORMAT`을 설정합니다. 허용되는 값은 `EPOCH`, `yyyy-MM-dd-HH-mm-ss` 또는 `yyyyMMddHHmmss`입니다. 이것은 입력 데이터 파일 명에 접미사로 붙는 타임스탬프 형식입니다. 이것은 Lookout Equipment에서 추론을 실행할 파일을 파악하는 데 사용됩니다 (그러므로 스케줄러가 실행할 파일을 찾게 하기 위해 이전 파일을 제거할 필요가 없음).* `COMPONENT_TIMESTAMP_DELIMITER`를 설정합니다. 허용되는 값은 `-`, `_` 또는 ` `입니다. 입력 파일 명의 타임스탬프에서 구성 요소를 분리할 때 사용하는 구분자입니다.	DATA_DELAY_OFFSET_IN_MINUTES = None INPUT_TIMEZONE_OFFSET = '+00:00' COMPONENT_TIMESTAMP_DELIMITER = '_' TIMESTAMP_FORMAT = 'yyyyMMddHHmmss'	_____no_output_____	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
추론 스케줄러 생성하기CreateInferenceScheduler API는 스케줄러를 생성하고 구동시킵니다. 즉, 즉각적으로 비용이 발생하기 시작합니다. 그러나 기존 스케줄러를 원하는대로 중지하거나 재구동시킬 수 있습니다 (이 노트북의 마지막 부분 참조).	scheduler = lookout.LookoutEquipmentScheduler( scheduler_name=INFERENCE_SCHEDULER_NAME, model_name=MODEL_NAME_FOR_CREATING_INFERENCE_SCHEDULER, region_name=REGION_NAME ) scheduler_params = { 'input_bucket': INFERENCE_DATA_SOURCE_BUCKET, 'input_prefix': INFERENCE_DATA_SOURCE_PREFIX, 'output_bucket': INFERENCE_DATA_OUTPUT_BUCKET, 'output_prefix': INFERENCE_DATA_OUTPUT_PREFIX, 'role_arn': ROLE_ARN_FOR_INFERENCE, 'upload_frequency': DATA_UPLOAD_FREQUENCY, 'delay_offset': DATA_DELAY_OFFSET_IN_MINUTES, 'timezone_offset': INPUT_TIMEZONE_OFFSET, 'component_delimiter': COMPONENT_TIMESTAMP_DELIMITER, 'timestamp_format': TIMESTAMP_FORMAT } scheduler.set_parameters(**scheduler_params)	_____no_output_____	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
추론 데이터 준비하기---스케줄러가 모니터링할 S3 입력 위치에 몇 가지 데이터를 준비하고 전송하겠습니다.	# 원본 신호 전체를 불러오겠습니다. all_tags_fname = os.path.join(DATA, 'training-data', 'expander.parquet') table = pq.read_table(all_tags_fname) all_tags_df = table.to_pandas() del table all_tags_df.head()	_____no_output_____	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
태그 설명을 불러옵시다. 본 데이터셋에는 다음 내용을 포함하는 태그 설명 파일이 존재합니다.* `Tag`: 이력 관리 시스템에 고객이 기록한 태그 명 (예컨대 [Honeywell 프로세스 이력 데이터베이스](https://www.honeywellprocess.com/en-US/explore/products/advanced-applications/uniformance/Pages/uniformance-phd.aspx))* `UOM`: 기록한 신호의 측정 단위* `Subsystem`: 해당 센서가 연결된 자산 부속의 ID여기에서 구성 요소 (즉, 하위 시스템 열)의 List를 수집할 수 있습니다.	tags_description_fname = os.path.join(RAW_DATA, 'tags_description.csv') tags_description_df = pd.read_csv(tags_description_fname) components = tags_description_df['Subsystem'].unique() tags_description_df.head()	_____no_output_____	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
샘플 추론 데이터셋을 구성하기 위해 원본 시계열 검증 기간에서 마지막 몇 분을 추출합니다.	# 추출하려는 시퀀스 개수 num_sequences = 3 # 스케줄링 빈도 (분): 이 값은 반드시 # 모델 학습에 사용한 리샘플링 비율에 맞춰 설정해야 합니다. frequency = 5 # 각 시퀀스를 반복합니다. start = all_tags_df.index.max() + datetime.timedelta(minutes=-frequency * (num_sequences) + 1) for i in range(num_sequences): end = start + datetime.timedelta(minutes=+frequency - 1) # 이전 5분 단위로 시간을 반올림합니다. tm = datetime.datetime.now() tm = tm - datetime.timedelta( minutes=tm.minute % frequency, seconds=tm.second, microseconds=tm.microsecond ) tm = tm + datetime.timedelta(minutes=+frequency * (i)) tm = tm - datetime.timedelta(hours=9) # KST에 따른 조정 current_timestamp = (tm).strftime(format='%Y%m%d%H%M%S') # 각 시퀀스마다 구성 요소 전체를 반복합니다. print(f'Extracting data from {start} to {end}:') new_index = None for component in components: # 해당 구성 요소와 특정 시간 범위에 대해 Dataframe을 추출합니다. signals = list(tags_description_df.loc[(tags_description_df['Subsystem'] == component), 'Tag']) signals_df = all_tags_df.loc[start:end, signals] # 스케줄러가 추론을 실행할 시간에 맞게끔 # 인덱스를 재설정해야 합니다. if new_index is None: new_index = pd.date_range( start=tm, periods=signals_df.shape[0], freq='1min' ) signals_df.index = new_index signals_df.index.name = 'Timestamp' signals_df = signals_df.reset_index() signals_df['Timestamp'] = signals_df['Timestamp'].dt.strftime('%Y-%m-%dT%H:%M:%S.%f') # 해당 파일을 CSV 형식으로 내보냅니다. component_fname = os.path.join(INFER_DATA, 'input', f'{component}_{current_timestamp}.csv') signals_df.to_csv(component_fname, index=None) start = start + datetime.timedelta(minutes=+frequency) # 입력 위치의 전체 폴더를 S3에 업로드합니다. INFERENCE_INPUT = os.path.join(INFER_DATA, 'input') !aws s3 cp --recursive --quiet $INFERENCE_INPUT s3://$BUCKET/$PREFIX/input # 이제 데이터를 준비했으므로 다음을 실행하여 스케줄러를 만듭니다. create_scheduler_response = scheduler.create()	Extracting data from 2015-11-30 23:45:00 to 2015-11-30 23:49:00: Extracting data from 2015-11-30 23:50:00 to 2015-11-30 23:54:00: Extracting data from 2015-11-30 23:55:00 to 2015-11-30 23:59:00: ===== Polling Inference Scheduler Status ===== Scheduler Status: PENDING Scheduler Status: RUNNING ===== End of Polling Inference Scheduler Status =====	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
스케줄러가 실행 중이며 추론 기록은 현재 비어 있습니다.![Scheduler created](../assets/schedule_created.png) 추론 결과 얻기--- 추론 실행 결과 나열하기 스케줄러가 추론을 최초로 실행할 경우 5-15분 정도 걸립니다. 대기가 끝나면 현재 추론 스케줄러에서 ListInferenceExecution API를 사용할 수 있습니다. 입력 파라미터로 스케줄러 명만 필요합니다.추론 실행 결과를 질의할 기간을 선택할 수 있습니다. 지정하지 않으면 추론 스케줄러에 대한 모든 실행 결과들이 나열됩니다. 시간 범위를 지정하려면 다음과 같이 합니다.```pythonSTART_TIME_FOR_INFERENCE_EXECUTIONS = datetime.datetime(2010,1,3,0,0,0)END_TIME_FOR_INFERENCE_EXECUTIONS = datetime.datetime(2010,1,5,0,0,0)```즉, `2010-01-03 00:00:00`부터 `2010-01-05 00:00:00`까지의 실행 결과들이 나열됩니다.특정 상태의 실행 결과를 질의하도록 선택할 수도 있습니다. 허용되는 상태는 `IN_PROGRESS`, `SUCCESS`와 `FAILED`입니다.	START_TIME_FOR_INFERENCE_EXECUTIONS = None END_TIME_FOR_INFERENCE_EXECUTIONS = None EXECUTION_STATUS = None execution_summaries = [] while len(execution_summaries) == 0: execution_summaries = scheduler.list_inference_executions( start_time=START_TIME_FOR_INFERENCE_EXECUTIONS, end_time=END_TIME_FOR_INFERENCE_EXECUTIONS, execution_status=EXECUTION_STATUS ) if len(execution_summaries) == 0: print('WAITING FOR THE FIRST INFERENCE EXECUTION') time.sleep(60) else: print('FIRST INFERENCE EXECUTED\n') break # execution_summaries	WAITING FOR THE FIRST INFERENCE EXECUTION WAITING FOR THE FIRST INFERENCE EXECUTION FIRST INFERENCE EXECUTED	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
스케줄러를 5분마다 실행하도록 구성했습니다. 몇 분 후 콘솔에서 첫 번째 실행 결과가 입력된 기록을 살펴볼 수 있습니다. ![Inference history](../assets/schedule_inference_history.png) 스케줄러가 시작될 때, 예를 들어 `datetime.datetime (2021, 1, 27, 9, 15)`일 때 입력 위치에서 단일 CSV 파일을 찾습니다. 여기에는 타임스탬프가 포함된 파일 명이, 말하자면 다음과 같은 파일 명이 존재해야 합니다.* subsystem-01_20210127091000.csv가 검색되고 수집됩니다.* subsystem-01_20210127091500.csv는 수집되지 않습니다 (다음 추론 실행 시 수집됨).`subsystem-01_20210127091000.csv` 파일을 연 다음 추론 실행의 DataStartTime과 DataEndTime 사이에 존재하는 시간 행을 찾습니다. 그러한 행을 찾지 못하면 내부 예외를 발생시킵니다. 실제 예측 결과 얻기 추론에 성공하면 CSV 파일이 버킷의 출력 위치에 저장됩니다. 각 추론은 `results.csv` 단일 파일이 존재하는 새 폴더를 만듭니다. 해당 파일을 읽고 여기에 내용을 표시해 보겠습니다.	results_df = scheduler.get_predictions() results_df.to_csv(os.path.join(INFER_DATA, 'output', 'results.csv')) results_df.head()	_____no_output_____	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
추론 스케줄러 운영--- 추론 스케줄러 중단하기근검 절약해야합니다. 스케줄러 실행이 Amazon Lookout for Equipment 비용의 주된 원인입니다. 다음 API를 이용하여 현재 실행 중인 추론 스케줄러를 중지시키세요. 그렇게 하면 주기적인 추론 실행이 중지됩니다.	scheduler.stop()	===== Polling Inference Scheduler Status ===== Scheduler Status: STOPPING Scheduler Status: STOPPED ===== End of Polling Inference Scheduler Status =====	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
추론 스케줄러 시작하기다음 API를 사용하여 `STOPPED` 추론 스케줄러를 재시작할 수 있습니다.	scheduler.start()	===== Polling Inference Scheduler Status ===== Scheduler Status: PENDING Scheduler Status: RUNNING ===== End of Polling Inference Scheduler Status =====	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
추론 스케줄러 삭제하기더 이상 사용하지 않는, 중지된 스케줄러를 삭제할 수 있습니다. 모델 당 하나의 스케줄러만 가질 수 있습니다.	scheduler.stop() scheduler.delete()	===== Polling Inference Scheduler Status ===== Scheduler Status: STOPPING Scheduler Status: STOPPED ===== End of Polling Inference Scheduler Status =====	MIT-0	notebooks/5_inference_scheduling.ipynb	youngmki/lookout-for-equipment-demo
RGBD integrationOpen3D implements a scalable RGBD image integration algorithm. The algorithm is based on the technique presented in [\[Curless1996\]](../reference.htmlcurless1996) and [\[Newcombe2011\]](../reference.htmlnewcombe2011). In order to support large scenes, we use a hierarchical hashing structure introduced in [Integrater in ElasticReconstruction](https://github.com/qianyizh/ElasticReconstruction/tree/master/Integrate). Read trajectory from .log fileThis tutorial uses the function `read_trajectory` to read a camera trajectory from a [.log file](http://redwood-data.org/indoor/fileformat.html). A sample `.log` file is as follows.``` examples/test_data/RGBD/odometry.log0 0 11 0 0 20 1 0 20 0 1 -0.30 0 0 11 1 20.999988 3.08668e-005 0.0049181 1.99962-8.84184e-005 0.999932 0.0117022 1.97704-0.0049174 -0.0117024 0.999919 -0.3004860 0 0 1```	class CameraPose: def __init__(self, meta, mat): self.metadata = meta self.pose = mat def __str__(self): return 'Metadata : ' + ' '.join(map(str, self.metadata)) + '\n' + \ "Pose : " + "\n" + np.array_str(self.pose) def read_trajectory(filename): traj = [] with open(filename, 'r') as f: metastr = f.readline() while metastr: metadata = list(map(int, metastr.split())) mat = np.zeros(shape=(4, 4)) for i in range(4): matstr = f.readline() mat[i, :] = np.fromstring(matstr, dtype=float, sep=' \t') traj.append(CameraPose(metadata, mat)) metastr = f.readline() return traj camera_poses = read_trajectory("../../test_data/RGBD/odometry.log")	_____no_output_____	MIT	examples/python/pipelines/rgbd_integration.ipynb	aaronlhe/Open3D
TSDF volume integrationOpen3D provides two types of TSDF volumes: `UniformTSDFVolume` and `ScalableTSDFVolume`. The latter is recommended since it uses a hierarchical structure and thus supports larger scenes.`ScalableTSDFVolume` has several parameters. `voxel_length = 4.0 / 512.0` means a single voxel size for TSDF volume is $\frac{4.0\mathrm{m}}{512.0} = 7.8125\mathrm{mm}$. Lowering this value makes a high-resolution TSDF volume, but the integration result can be susceptible to depth noise. `sdf_trunc = 0.04` specifies the truncation value for the signed distance function (SDF). When `color_type = TSDFVolumeColorType.RGB8`, 8 bit RGB color is also integrated as part of the TSDF volume. Float type intensity can be integrated with `color_type = TSDFVolumeColorType.Gray32` and `convert_rgb_to_intensity = True`. The color integration is inspired by [PCL](http://pointclouds.org/).	volume = o3d.pipelines.integration.ScalableTSDFVolume( voxel_length=4.0 / 512.0, sdf_trunc=0.04, color_type=o3d.pipelines.integration.TSDFVolumeColorType.RGB8) for i in range(len(camera_poses)): print("Integrate {:d}-th image into the volume.".format(i)) color = o3d.io.read_image("../../test_data/RGBD/color/{:05d}.jpg".format(i)) depth = o3d.io.read_image("../../test_data/RGBD/depth/{:05d}.png".format(i)) rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth( color, depth, depth_trunc=4.0, convert_rgb_to_intensity=False) volume.integrate( rgbd, o3d.camera.PinholeCameraIntrinsic( o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault), np.linalg.inv(camera_poses[i].pose))	_____no_output_____	MIT	examples/python/pipelines/rgbd_integration.ipynb	aaronlhe/Open3D
Extract a meshMesh extraction uses the marching cubes algorithm [\[LorensenAndCline1987\]](../reference.htmllorensenandcline1987).	print("Extract a triangle mesh from the volume and visualize it.") mesh = volume.extract_triangle_mesh() mesh.compute_vertex_normals() o3d.visualization.draw_geometries([mesh], front=[0.5297, -0.1873, -0.8272], lookat=[2.0712, 2.0312, 1.7251], up=[-0.0558, -0.9809, 0.1864], zoom=0.47)	_____no_output_____	MIT	examples/python/pipelines/rgbd_integration.ipynb	aaronlhe/Open3D
Apache Arrow 1 Compare performance of csv, Parquet and Arrow - 1 Change	import pyarrow.parquet as pq import pyarrow as pa import pandas as pd import numpy as np import os import psutil	_____no_output_____	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.1 Load and prepare data One more change	## Read Palmer Station Penguin dataset from GitHub df = pd.read_csv("https://raw.githubusercontent.com/allisonhorst/" "palmerpenguins/47a3476d2147080e7ceccef4cf70105c808f2cbf/" "data-raw/penguins_raw.csv") # Increase dataset to 1m rows and reset index df = df.sample(1_000_000, replace=True).reset_index(drop=True) # Update sample number (0 to 999'999) df["Sample Number"] = df.index # Add some random variation to numeric columns df[["Culmen Length (mm)", "Culmen Depth (mm)", "Flipper Length (mm)", "Body Mass (g)"]] = df[["Culmen Length (mm)", "Culmen Depth (mm)", "Flipper Length (mm)", "Body Mass (g)"]] \ + np.random.rand(df.shape[0], 4) # Create dataframe where missing numeric values are filled with zero df_nonan = df.copy() df_nonan[["Culmen Length (mm)", "Culmen Depth (mm)", "Flipper Length (mm)", "Body Mass (g)"]] = df[["Culmen Length (mm)", "Culmen Depth (mm)", "Flipper Length (mm)", "Body Mass (g)"]].fillna(0)	_____no_output_____	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.2 Write to disk	# Write to csv df.to_csv("penguin-dataset.csv") # Write to parquet df.to_parquet("penguin-dataset.parquet") context = pa.default_serialization_context() # Write to Arrow # Convert from pandas to Arrow table = pa.Table.from_pandas(df) # Write out to file writer = pa.RecordBatchFileWriter('penguin-dataset.arrow', table.schema) writer.write(table) writer.close() #with pa.OSFile('penguin-dataset.arrow', 'wb') as sink: #with pa.RecordBatchFileWriter(sink, table.schema,write_legacy_format=True) as writer: #writer.write_table(table) # Convert from no-NaN pandas to Arrow table_nonan = pa.Table.from_pandas(df_nonan) # Write out to file writer = pa.RecordBatchFileWriter('penguin-dataset-nonan.arrow', table.schema) writer.write(table_nonan) writer.close() #with pa.OSFile('penguin-dataset-nonan.arrow', 'wb') as sink: #with pa.RecordBatchFileWriter(sink, table_nonan.schema,write_legacy_format=True) as writer: #writer.write_table(table_nonan)	_____no_output_____	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.3 Reading time - calculate average of numeric column 1.3.1 Read csv and calculate mean	%%timeit pd.read_csv("penguin-dataset.csv")["Flipper Length (mm)"].mean()	3.4 s ± 105 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.3.2 Read parquet and calculate mean	%%timeit pd.read_parquet("penguin-dataset.parquet", columns=["Flipper Length (mm)"]).mean()	/Users/ravishankarnair/anaconda3/envs/py36/lib/python3.6/site-packages/pyarrow/pandas_compat.py:708: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead. labels = getattr(columns, 'labels', None) or [ /Users/ravishankarnair/anaconda3/envs/py36/lib/python3.6/site-packages/pyarrow/pandas_compat.py:735: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead return pd.MultiIndex(levels=new_levels, labels=labels, names=columns.names) /Users/ravishankarnair/anaconda3/envs/py36/lib/python3.6/site-packages/pyarrow/pandas_compat.py:752: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead. labels, = index.labels	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.3.3 Read Arrow using file API	%%timeit with pa.OSFile('penguin-dataset.arrow', 'rb') as source: table = pa.ipc.open_file(source).read_all().column("Flipper Length (mm)") result = table.to_pandas().mean()	133 ms ± 2.73 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.3.4 Read Arrow with memory-mapped API with missing values	%%timeit source = pa.memory_map('penguin-dataset.arrow', 'r') table = pa.ipc.RecordBatchFileReader(source).read_all().column("Flipper Length (mm)") result = table.to_pandas().mean()	6.19 ms ± 82.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.3.5 Read Arrow with memory-mapped API without missing values (zero-copy)	%%timeit source = pa.memory_map('penguin-dataset-nonan.arrow', 'r') table = pa.ipc.RecordBatchFileReader(source).read_all().column("Flipper Length (mm)") result = table.to_pandas().mean()	4.04 ms ± 80.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.4 Memory consumption - read column	# Measure initial memory consumption memory_init = psutil.Process(os.getpid()).memory_info().rss >> 20	_____no_output_____	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.4.1 Read csv	col_csv = pd.read_csv("penguin-dataset.csv")["Flipper Length (mm)"] memory_post_csv = psutil.Process(os.getpid()).memory_info().rss >> 20	_____no_output_____	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.4.2 Read parquet	col_parquet = pd.read_parquet("penguin-dataset.parquet", columns=["Flipper Length (mm)"]) memory_post_parquet = psutil.Process(os.getpid()).memory_info().rss >> 20	_____no_output_____	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.4.3 Read Arrow using file API	with pa.OSFile('penguin-dataset.arrow', 'rb') as source: col_arrow_file = pa.ipc.open_file(source).read_all().column("Flipper Length (mm)").to_pandas() memory_post_arrowos = psutil.Process(os.getpid()).memory_info().rss >> 20	_____no_output_____	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.4.4 Read Arrow with memory-mapped API with missing values	source = pa.memory_map('penguin-dataset.arrow', 'r') table_mmap = pa.ipc.RecordBatchFileReader(source).read_all().column("Flipper Length (mm)") col_arrow_mapped = table_mmap.to_pandas() memory_post_arrowmmap = psutil.Process(os.getpid()).memory_info().rss >> 20	_____no_output_____	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.4.5 Read Arrow with memory-mapped API without missing values (zero-copy)	source = pa.memory_map('penguin-dataset-nonan.arrow', 'r') table_mmap_zc = pa.ipc.RecordBatchFileReader(source).read_all().column("Flipper Length (mm)") col_arrow_mapped_zc = table_mmap_zc.to_pandas() memory_post_arrowmmap_zc = psutil.Process(os.getpid()).memory_info().rss >> 20	_____no_output_____	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
1.4.6 Display memory consupmtion	# Print memory consumption print(f"csv: {memory_post_csv - memory_init}\n" f"Parquet: {memory_post_parquet - memory_post_csv}\n" f"Arrow file API: {memory_post_arrowos - memory_post_parquet}\n" f"Arrow memory-mapped API with NaNs: {memory_post_arrowmmap - memory_post_arrowos}\n" f"Arrow memory-mapped API (zero-copy): {memory_post_arrowmmap_zc - memory_post_arrowmmap}\n")	csv: 223 Parquet: -4 Arrow file API: -8 Arrow memory-mapped API with NaNs: 8 Arrow memory-mapped API (zero-copy): 0	Apache-2.0	arrow_performance_comparison_notebook.ipynb	passionbytes/arrowexp
Controlling accesss to attributes * Following blocks are one possible implementation of vectors of `double`s. * Here, member variable `new_name` is in `protected:` part.* Member methods and subclass members can access this variable but from the outside of the class, we cannot access it.* We call it encapsulation; instead of directly reading or writing to the variable, we would use mutator or reader methods.* This is because to modularize software components to the level of integrated circuit chips. ``` C++// Begin vector_double.hinclude include include include include include // This directive would activate method call loggingifndef LOGdefine LOGendif// This directive woudl activate bracket [] operator logging// Added this just because the examples call [] operator frequentlyifndef LOGBRACKET// define LOGBRACKETendif// This is to prevent declaring vector class twice// If declared twice, C/C++ compilers would show an error messageifndef VECTOR_DOUBLEdefine VECTOR_DOUBLEclass RowVector { // automatic allocation // https://stackoverflow.com/questions/8553464/vector-as-a-class-member std::vector columns; protected: // To distinguish vectors from each other std::string name; public: // Default constructor RowVector(); // Destructor ~ RowVector(); // Default arguments // If the function could not find the argument in the call, it uses the default value. RowVector(const uint32_t n, const double values=NULL, std::string new_name="None"); // Whenever possible, it is advisible to use `const` keyword // Protects data from being overwritten and may optimize further RowVector(const uint32_t n, std::string new_name="None"); // Copy constructor must use a reference. // What would happen otherwise? RowVector(const RowVector & other); // Two versions of [] operators // This one is for normal vectors. Allows changing values double & operator [] (const uint32_t i); // This one is for constant vectors. Protects the values from overwriting double operator [] (const uint32_t i) const; const std::string get_name() const; RowVector operator + (const RowVector & other); RowVector operator (const double a); const double operator * (const RowVector & other); void show(); void resize(std::size_t new_size); std::size_t size() const noexcept; RowVector & operator += (const RowVector & other); RowVector & operator = (const double a);};endif// End vector_double.h``` ``` C++// Begin vector_double.cppinclude include include include include include include "vector_double.h"RowVector::RowVector(){// This may look involving but sometimes helps how the program works.ifdef LOG std::cout << '[' << &columns << ']' << "RowVector()" << '\n';endif name = "None";}RowVector::~ RowVector(){ifdef LOG std::cout << '[' << &columns << ']' << "~ RowVector()" << '\n';endif}RowVector::RowVector(const uint32_t n, const double values, std::string new_name){ifdef LOG std::cout << '[' << &columns << ']' << "RowVector(" << n << ", " << values << ", " << new_name << ")\n";endif columns.resize(n); // If initial values available, copy if (values){ for (uint32_t i = 0; columns.size() > i; ++i){ columns[i] = values[i]; } } // If no initial values, set all values zero else{ for (uint32_t i = 0; columns.size() > i; ++i){ columns[i] = 0.0; } } name = new_name;}// Instead of implementing another constructor, calling an existing one// c++ 11 or laterRowVector::RowVector(const uint32_t n, std::string new_name) : RowVector(n, NULL, new_name){ifdef LOG std::cout << '[' << &columns << ']' << "RowVector(" << n << ", " << new_name << ")\n";endif}RowVector::RowVector(const RowVector & other){ifdef LOG std::cout << '[' << &columns << ']' << "RowVector(" << & other << ")\n";endif // https://codereview.stackexchange.com/questions/149669/c-operator-overloading-for-matrix-operations-follow-up // http://www.cplusplus.com/reference/vector/vector/resize/ columns.resize(other.columns.size()); // element loop for(uint32_t i=0; columns.size() > i; ++i){ columns[i] = other.columns[i]; } // Copy name of the other one name = other.name; // Then append name.append("2");}double & RowVector::operator [] (const uint32_t i){ifdef LOGBRACKET std::cout << '[' << &columns << ']' << "double & RowVector::operator [] (" << i << ")\n";endif // Return reference; otherwise, unable to assign return columns[i];}double RowVector::operator [] (const uint32_t i) const {ifdef LOGBRACKET std::cout << '[' << &columns << ']' << "double RowVector::operator [] (" << i << ") const\n";endif // Return reference; otherwise, unable to assign return columns[i];}const std::string RowVector::get_name() const{ifdef LOG std::cout << '[' << &columns << ']' << "const std::string RowVector::get_name()\n";endif // Return constant; to prevent change return name;}RowVector RowVector::operator + (const RowVector & other){ifdef LOG std::cout << '[' << &columns << ']' << "RowVector RowVector::operator + (" << & other << ")\n";endif // Check size assert(columns.size() == other.columns.size()); // Make a new vector to return RowVector temp(other); // Element loop for (uint32_t i=0; columns.size() > i; ++i){ temp[i] += columns[i]; } // Returning a temporary image return temp;}RowVector RowVector::operator * (const double a){ifdef LOG std::cout << '[' << &columns << ']' << "RowVector RowVector::operator * (" << a << ")\n";endif // Make a new vector to return RowVector temp(this); // Element loop in `for each` style // c++ 11 or later for (auto & element : temp.columns){ element = a; } // Returning a temporary image return temp;}const double RowVector::operator * (const RowVector & other){ifdef LOG std::cout << '[' << &columns << ']' << "const double RowVector::operator * (" << & other << ")\n";endif // Check size assert(columns.size() == other.columns.size()); double dot_product = 0.0; // Element loop for (uint32_t i = 0; columns.size() > i; ++i){ dot_product += columns[i] * other.columns[i]; } // Returning a temporary image return dot_product;}void RowVector::show(){ifdef LOG std::cout << '[' << &columns << ']' << "void RowVector::show()\n";endif for (uint32_t i=0; columns.size()> i; ++i){ std::cout << name << '[' << i << "] = " << columns[i] << '\n'; }}void RowVector::resize(std::size_t new_size){ifdef LOG std::cout << '[' << &columns << ']' << "void RowVector::resize(" << new_size << ")\n";endif columns.resize(new_size);}std::size_t RowVector::size() const noexcept{ifdef LOG std::cout << '[' << &columns << ']' << "std::size_t RowVector::size() const noexcept\n";endif return columns.size();}RowVector & RowVector::operator += (const RowVector & other) {ifdef LOG std::cout << '[' << &columns << ']' << "RowVector & RowVector::operator += (" << & other << ")\n";endif // https://stackoverflow.com/questions/4581961/c-how-to-overload-operator for (uint32_t i=0; size()>i; ++i){ columns[i] += other[i]; } return this;}RowVector & RowVector::operator = (const double a) {ifdef LOG std::cout << '[' << &columns << ']' << "RowVector & RowVector::operator = (" << a << ")\n";endif // https://stackoverflow.com/questions/4581961/c-how-to-overload-operator for (uint32_t i=0; size()>i; ++i){ columns[i] = a; } return this;}// End vector_double.cpp// Build command : g++ -Wall -g -std=c++14 vector_double.cpp -fsyntax-only``` ``` C++// Begin cpp_vector_double_practice.cppinclude include include include include include include "vector_double.h"int32_t main(int32_t argn, char argv[]){ double s[] = {1.0, 2.0}; std::cout << "RowVector row (2u, s, \"row\");\n"; RowVector row (2u, s, "row"); row.show(); std::cout << "RowVector another_row (row);\n"; RowVector another_row (row); row.show(); another_row.show(); std::cout << "another_row[1] += 0.5;\n"; another_row[1] += 0.5; row.show(); another_row.show(); std::cout << "RowVector row_plus_another(row + another_row);\n"; RowVector row_plus_another(row + another_row); row.show(); another_row.show(); row_plus_another.show(); std::cout << "RowVector zeros(3);\n"; RowVector zeros(3u, "zeros"); row.show(); another_row.show(); row_plus_another.show(); zeros.show(); double t[] = {2.0, -1.0}; RowVector ortho (2u, t, "ortho"); double dot = row * ortho; std::cout << "double dot = row * ortho;\n"; std::cout << "dot = " << dot << '\n'; std::cout << "dot = row * row;\n"; dot = row * row; std::cout << "dot = " << dot << '\n';}// End cpp_vector_double_practice.cpp// Build command : g++ -Wall -g -std=c++14 cpp_vector_double_practice.cpp vector_double.cpp -o cpp_vector_double_practice``` * In the mean while, following code blocks depict a possible implementation in python.	import collections class Vector(collections.UserList): def __add__(self, other): # check size assert len(self) == len(other), f"Lengths are different ({len(self)} == {len(other)})" # trying list comprehension return Vector([a + b for a, b in zip(self, other)]) def __radd__(self, other): # What is this? return self.__add__(other) def __mul__(self, other): # what is happening here? if isinstance(other, (int, float, complex)): result = Vector([a * other for a in self]) elif isinstance(other, Vector): assert len(self) == len(other), f"Lengths are different ({len(self)} == {len(other)})" result = sum(a * b for a, b in zip(self, other)) return result def __rmul__(self, other): return __mul__(self, other) def __str__(self): # How does the .join() work? return '\n'.join(f"{hex(id(self))}[{i}] = {self[i]}" for i in range(len(self))) def __len__(self): return len(self.data) print("a = Vector([1, 2])") a = Vector([1, 2]) print(a) print("b = Vector(a)") b = Vector(a) print(a) print(b) print("b[1] += (-0.5)") b[1] += (-0.5) print(a) print(b) print("c = a + b") c = a + b print(a) print(b) print(c) print("ortho = Vector([2, -1])") ortho = Vector([2, -1]) print(a) print(b) print(c) print(ortho) print("dot = a * ortho") dot = a * ortho print(f"a * ortho = {dot}") print("dot = a * a") dot = a * a print(f"a * a = {dot}")	_____no_output_____	BSD-3-Clause	02.ipynb	2018pycpp/18pycpp-04
Matrix class example In C++ * Following code blocks present a possible implementation of matrix class in C++.* Please note that to build these files, `vector_double.h` and `vector_double.cpp` files are necessary. ```C++// Begin matrix_double.hinclude include include include include include include "vector_double.h"ifndef MATRIX_DOUBLEdefine MATRIX_DOUBLEclass Matrix{ std::vector rows; protected: std::string name; public: Matrix(); ~ Matrix(); Matrix(const uint32_t m, const uint32_t n, const double values, std::string new_name="None"); Matrix(const uint32_t m, const uint32_t n, std::string new_name="None"); Matrix(const Matrix & other, std::string new_name=""); Matrix(const RowVector & other, std::string new_name=""); RowVector & operator [] (const uint32_t i); const RowVector operator [] (const uint32_t i) const; const std::string get_name() const; Matrix operator + (const Matrix & other); Matrix operator (const double a); RowVector operator * (const RowVector &v); Matrix operator * (const Matrix & other); void show(); Matrix transpose(); const size_t get_height() const; const size_t get_width() const;};endif// End matrix_double.h``` ``` C++// Begin matrix_double.cppinclude include include include include include include "vector_double.h"include "matrix_double.h"Matrix::Matrix(){ifdef LOG std::cout << '[' << &rows << ']' << "Matrix()" << '\n';endif name = "None";}Matrix::~ Matrix(){ifdef LOG std::cout << '[' << &rows << ']' << "~ Matrix()" << '\n';endif}Matrix::Matrix(const uint32_t m, const uint32_t n, const double values, std::string new_name){ifdef LOG std::cout << '[' << &rows << ']' << "Matrix(" << m << ", "<< n << ", " << values << ", " << new_name << ")\n";endif name = new_name; rows.resize(m); // If initial values available, copy if (values){ // row loop for (uint32_t i = 0; m > i; ++i){ rows[i].resize(n); // column loop for (uint32_t j = 0; n > j; ++j){ rows[i][j] = (values + i * n + j) ; } } } // If no initial values, set all values zero else{ // row loop for (uint32_t i = 0; m > i; ++i){ rows[i].resize(n); // column loop for (uint32_t j = 0; n > j; ++j){ rows[i][j] = 0.0; } } }}// Instead of implementing another constructor, calling an existing one// c++ 11 or laterMatrix::Matrix(const uint32_t m, const uint32_t n, std::string new_name) : Matrix(m, n, NULL, new_name){ifdef LOG std::cout << '[' << &rows << ']' << "Matrix(" << m << ", " << n << ", " << new_name << ")\n";endif}Matrix::Matrix(const Matrix & other, std::string new_name){ifdef LOG std::cout << '[' << &rows << ']' << "Matrix(" << & other << ")\n";endif // https://codereview.stackexchange.com/questions/149669/c-operator-overloading-for-matrix-operations-follow-up // http://www.cplusplus.com/reference/vector/vector/resize/ rows.resize(other.rows.size()); // row loop for(uint32_t i=0; rows.size() > i; ++i){ rows[i].resize(other.rows[i].size()); // column loop for(uint32_t j=0; other.rows[i].size() > j; ++j){ // Another possibility is as follows // rows[i][j] = other.rows[i][j]; // However for now the line above would create a temporary row vector // To avoid seemingly unnecessary such temporary object, // for now would use the following line rows[i][j] = other.rows[i][j]; } } if ("" != new_name){ name = new_name; } else{ // Copy name of the other one name = other.name; // Then append name.append("2"); }}Matrix::Matrix(const RowVector & other, std::string new_name){ // RowVector -> n x 1 matrix ifdef LOG std::cout << '[' << &rows << ']' << "Matrix(const RowVector &" << & other << ")\n";endif rows.resize(other.size()); // row loop for(uint32_t i=0; rows.size() > i; ++i){ rows[i].resize(1); rows[i][0] = other[0]; } if ("" != new_name){ name = new_name; } else{ // Copy name of the other one name = other.get_name(); // Then append name.append("2"); }}RowVector & Matrix::operator [] (const uint32_t i){ifdef LOGBRACKET std::cout << '[' << &rows << ']' << "RowVector & Matrix::operator [] (" << i << ")\n";endif // Return reference; otherwise, unable to assign return rows[i];}const RowVector Matrix::operator [] (const uint32_t i) const {ifdef LOGBRACKET std::cout << '[' << &rows << ']' << "const RowVector Matrix::operator [] (" << i << ")\n";endif // Return reference; otherwise, unable to assign return rows[i];}const std::string Matrix::get_name() const{ifdef LOG std::cout << '[' << &rows << ']' << "const std::string Matrix::get_name()\n";endif // Return constant; to prevent change return name;}Matrix Matrix::operator + (const Matrix & other){ifdef LOG std::cout << '[' << &rows << ']' << "Matrix Matrix::operator + ("<< & other <<")\n";endif // Check size assert(this->get_height() == other.get_height()); assert(this->get_width() == other.get_width());ifdef LOG std::cout << "Matrix temp(other);\n";endif // Make a new vector to return Matrix temp(other, get_name() + '+' + other.get_name());ifdef LOG std::cout << "Begin row loop\n";endif // Row loop for (uint32_t i=0; rows.size() > i; ++i){ temp[i] += rows[i]; }ifdef LOG std::cout << "End row loop\n";endif // Returning a temporary image return temp;}Matrix Matrix::operator * (const double a){ifdef LOG std::cout << '[' << &rows << ']' << "Matrix Matrix::operator * (" << a << ")\n";endif // Make a new vector to return // https://stackoverflow.com/questions/332111/how-do-i-convert-a-double-into-a-string-in-c Matrix temp(this, std::to_string(a) + '' + get_name()); // Element loop in `for each` style // c++ 11 or later for (auto & element : temp.rows){ element = a; } // Returning a temporary image return temp;}RowVector Matrix::operator (const RowVector &v){ifdef LOG std::cout << '[' << &rows << ']' << "Matrix Matrix::operator * (" << &v << ")\n";endif // Make a new vector to return RowVector temp(rows.size(), NULL, name + '' + v.get_name()); // Element loop in `for each` style // c++ 11 or later for (uint32_t i=0; rows.size()>i; ++i){ temp[i] = rows[i] v; } // Returning a temporary image return temp;}Matrix Matrix::operator * (const Matrix & other){ifdef LOG std::cout << '[' << &rows << ']' << "Matrix Matrix::operator * (" << &other << ")\n";endif // Check size assert(rows[0].size() == other.rows.size()); Matrix temp(rows.size(), other[0].size(), name + '' + other.name); // row loop for (uint32_t i = 0; rows.size() > i; ++i){ // column loop for(uint32_t j = 0; other[0].size() > j; ++j){ // dummy index loop for(uint32_t k = 0; rows[0].size() > k; ++k){ temp[i][j] += rows[i][k] other[k][j]; } } } // Returning a temporary image return temp;}void Matrix::show(){ifdef LOG std::cout << '[' << &rows << ']' << "void Matrix::show()\n";endif // row loop for (uint32_t i=0; rows.size()> i; ++i){ // column loop for (uint32_t j=0; rows[i].size()> j; ++j){ std::cout << get_name() << '['<< i << "][" << j << "]= " << rows[i][j] << '\n'; } }}Matrix Matrix::transpose(){ifdef LOG std::cout << '[' << &rows << ']' << "Matrix Matrix::transpose()\n";endif Matrix temp(rows[0].size(), rows.size(), name+"T"); // row loop for(uint32_t i=0; temp.rows.size()> i; ++i){ // column loop for(uint32_t j=0; temp.rows.size()> j; ++j){ temp[i][j] = rows[i][j]; } } return temp;}const size_t Matrix::get_height() const{ return rows.size();}const size_t Matrix::get_width() const{ return rows[0].size();}// End matrix_double.cpp// Build command : g++ -Wall -g -std=c++14 matrix_double.cpp -fsyntax-only``` ``` C++// Begin cpp_matrix_double_practice.cppinclude include include include include include include include "matrix_double.h"int32_t main(int32_t argn, char argv[]){ double s[] = {1.0, 0.0, 0.0, 1.0}; std::cout << "Matrix id (2u, 2u, s, \"identity\");\n"; Matrix identity (2u, 2u, s, "id"); identity.show(); double r[] = {+cos(M_PI/6.0), sin(M_PI/6.0), -sin(M_PI/6.0), cos(M_PI/6.0)}; std::cout << "Matrix rotation (2u, 2u, r, \"rot\");\n"; Matrix rotation (2u, 2u, r, "rot"); identity.show(); rotation.show(); std::cout << "Matrix sum(identity + rotation);\n"; Matrix sum(identity + rotation); identity.show(); rotation.show(); sum.show(); // Check sum operation result for (uint32_t i=0; 2u > i; ++i){ for (uint32_t j=0; 2u > j; ++j){ assert(sum[i][j] == (identity[i][j] + rotation[i][j])); } } std::cout << "Matrix twice(identity 2.0);\n"; Matrix twice(identity * 2.0); // Check scala multiplication result assert(twice[0][0] == 2.0); assert(twice[0][1] == 0.0); assert(twice[1][0] == 0.0); assert(twice[1][1] == 2.0); std::cout << "Matrix new_axis(twice * rotation);\n"; Matrix new_axis(twice * rotation); // Check matrix multiplication result for (uint32_t i=0; 2u > i; ++i){ for (uint32_t j=0; 2u > j; ++j){ assert(new_axis[i][j] == (2.0 * rotation[i][j])); } } Matrix ninety_degrees(rotation * rotation * rotation); // Check matrix multiplication result assert(abs(ninety_degrees[0][0] - ( 0.0)) < 1e-12); assert(abs(ninety_degrees[0][1] - ( 1.0)) < 1e-12); assert(abs(ninety_degrees[1][0] - (-1.0)) < 1e-12); assert(abs(ninety_degrees[1][1] - ( 0.0)) < 1e-12); // State Space Representation Ax + B u double xi_d[] = {1.0, 0.0}; double ones_d[] = {1.0, 1.0}; Matrix xi(2, 1, xi_d, "xi"); Matrix B(2, 1, ones_d, "B"); double u = 0.75; Matrix xj; // xj = A xi + B u xj = rotation * xi + B * u; xj.show(); assert(abs(xj[0][0] - ( 0.75 + cos(M_PI/6.0))) < 1e-12); assert(abs(xj[1][0] - ( 0.75 - sin(M_PI/6.0))) < 1e-12);}// End cpp_matrix_double_practice.cpp// Build command : g++ -Wall -g -std=c++14 cpp_matrix_double_practice.cpp vector_double.cpp matrix_double.cpp -o cpp_matrix_double_practice``` * The build command above lists necessary files. In Python * Following code blocks are a possible implementation of matrix in python.* As in C++ example, it will build on the prior `Vector` class.	import collections import copy class Matrix(collections.UserList): def __init__(self, m=None, n=None, values=None): if m is None: self.m = self.n = 0 self.data = [] elif values is not None: self.m = int(m) # number of rows self.n = int(n) # number of columns # Again utilizing Vector class and list comprehension self.data = [Vector(values[(i * n):((i+1) * n)]) for i in range(m)] elif n is None: if isinstance(m, Matrix): # copy constructor self.m = m.m self.n = m.n # To avoid referencing rows of m matrix self.data = copy.deepcopy(m.data) elif isinstance(m, Vector): # Vector to n x 1 Matrix self.data = [Vector([value]) for value in m] self.m = len(self.data) self.n = 1 elif isinstance(m, int) and isinstance(n, int) and values is None: # zeros self.m = m self.n = n self.data = [Vector([0.0] * n) for i in range(m)] else: raise NotImplementedError def __add__(self, other): assert isinstance(other, Matrix) result = Matrix() for self_row, other_row in zip(self, other): result.append(self_row + other_row) return result def __mul__(self, other): if isinstance(other, (int, float, complex)): result = Matrix() for row in self: result.append(row * other) elif isinstance(other, Matrix): assert self.n == other.m, f"Matrix sizes ({self.m}, {self.n}) x ({other.m}, {other.n}) not compatible" result = Matrix(self.m, other.n) for i in range(self.m): for j in range(other.n): for k in range(self.n): result[i][j] += self[i][k] * other[k][j] elif isinstance(other, Vector): assert self.n == len(other), f"Matrix sizes ({self.m}, {self.n}) x ({len(other)}, 1) not compatible" result = Vector([row * other for row in self]) else: raise NotImplementedError return result def __str__(self): row_text = [] for i, row in enumerate(self): for j, value in enumerate(row): row_text.append(f"{hex(id(self))}[{i}][{j}] = {self[i][j]}") return '\n'.join(row_text) def transpose(self): result = Matrix() result.data = list(zip(self.data)) result.m = self.n resutl.n = self.m matA = Matrix(2, 2, list(range(4))) print(matA) matB = Matrix(matA) matB[0][0] = matA[0][0] + 7 print(matA) print(matB) assert matA[0][0] != matB[0][0], "Please use deep copy" vecC = Vector([1, 0]) print("matC = Matrix(vecC)") matC = Matrix(vecC) print(matA) print(matB) print(matC) print("matD = Matrix(2, 2)") matD = Matrix(2, 2) print(matA) print(matB) print(matC) print(matD) for i in range(matD.m): for j in range(matD.n): assert 0 == matD[i][j] print("matE = matA + matA") matE = matA + matA print(matA) print(matB) print(matC) print(matD) print(matE) for i in range(matE.m): for j in range(matE.n): assert matE[i][j] == 2 * matA[i][j] print("matF = matA * matA") matF = matA * matA print(matA) print(matB) print(matC) print(matD) print(matE) print(matF) print("matG = matA * vecC") vecG = matA * vecC print(matA) print(matB) print(matC) print(matD) print(matE) print(matF) print(vecG) assert len(vecG) == matA.m for i in range(matA.m): assert vecG[i] == matA[i][0]	_____no_output_____	BSD-3-Clause	02.ipynb	2018pycpp/18pycpp-04
Overview- nb023 ベース- nb034の結果を使う Const	NB = '035' isSmallSet = False if isSmallSet: LENGTH = 7000 else: LENGTH = 500_000 PATH_TRAIN = './../data/input/train_clean.csv' PATH_TEST = './../data/input/test_clean.csv' PATH_SMPLE_SUB = './../data/input/sample_submission.csv' DIR_OUTPUT = './../data/output/' cp = ['#f8b195', '#f67280', '#c06c84', '#6c5b7b', '#355c7d'] sr = 1010*3 # 10 kHz	_____no_output_____	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
Import everything I need :)	import warnings warnings.filterwarnings('ignore') import time import gc import random import os import itertools import multiprocessing import numpy as np from scipy import signal # from pykalman import KalmanFilter import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from fastprogress import progress_bar from lightgbm import LGBMRegressor from sklearn.model_selection import KFold, train_test_split, StratifiedKFold, GroupKFold from sklearn.metrics import f1_score, mean_absolute_error, confusion_matrix from sklearn.ensemble import RandomForestRegressor from sklearn.tree import DecisionTreeRegressor # from sklearn.svm import SVR from sklearn.linear_model import Lasso # from dtreeviz.trees import dtreeviz import tensorflow as tf from tensorflow.keras.layers import * from tensorflow.keras.callbacks import Callback, LearningRateScheduler from tensorflow.keras.losses import categorical_crossentropy from tensorflow.keras.optimizers import Adam from tensorflow.keras import backend as K from tensorflow.keras import losses, models, optimizers # import tensorflow_addons as tfa	_____no_output_____	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
My function	def f1_macro(true, pred): return f1_score(true, pred, average='macro') def get_df_batch(df, batch): idxs = df['batch'] == batch assert any(idxs), 'そのようなbatchはありません' return df[idxs] def get_signal_mv_mean(df, n=3001): signal_mv = np.zeros(len(df)) for bt in df['batch'].unique(): idxs = df['batch'] == bt _signal_mv = df['signal'][idxs].rolling(n, center=True).mean().interpolate('spline', order=5, limit_direction='both').values signal_mv[idxs] = _signal_mv return signal_mv def get_signal_mv_std(df, n=3001): signal_mv = np.zeros(len(df)) for bt in df['batch'].unique(): idxs = df['batch'] == bt _signal_mv = df['signal'][idxs].rolling(n, center=True).std().interpolate('spline', order=5, limit_direction='both').values signal_mv[idxs] = _signal_mv return signal_mv def get_signal_mv_min(df, n=3001): signal_mv = np.zeros(len(df)) for bt in df['batch'].unique(): idxs = df['batch'] == bt _signal_mv = df['signal'][idxs].rolling(n, center=True).min().interpolate('spline', order=5, limit_direction='both').values signal_mv[idxs] = _signal_mv return signal_mv def get_signal_mv_max(df, n=3001): signal_mv = np.zeros(len(df)) for bt in df['batch'].unique(): idxs = df['batch'] == bt _signal_mv = df['signal'][idxs].rolling(n, center=True).max().interpolate('spline', order=5, limit_direction='both').values signal_mv[idxs] = _signal_mv return signal_mv def group_feat_train(_train): train = _train.copy() # group init train['group'] = int(0) # group 1 idxs = (train['batch'] == 3) \| (train['batch'] == 7) train['group'][idxs] = int(1) # group 2 idxs = (train['batch'] == 5) \| (train['batch'] == 8) train['group'][idxs] = int(2) # group 3 idxs = (train['batch'] == 2) \| (train['batch'] == 6) train['group'][idxs] = int(3) # group 4 idxs = (train['batch'] == 4) \| (train['batch'] == 9) train['group'][idxs] = int(4) return train[['group']] def group_feat_test(_test): test = _test.copy() # group init test['group'] = int(0) x_idx = np.arange(len(test)) # group 1 idxs = (100000<=x_idx) & (x_idx<200000) test['group'][idxs] = int(1) idxs = (900000<=x_idx) & (x_idx<=1000000) test['group'][idxs] = int(1) # group 2 idxs = (200000<=x_idx) & (x_idx<300000) test['group'][idxs] = int(2) idxs = (600000<=x_idx) & (x_idx<700000) test['group'][idxs] = int(2) # group 3 idxs = (400000<=x_idx) & (x_idx<500000) test['group'][idxs] = int(3) # group 4 idxs = (500000<=x_idx) & (x_idx<600000) test['group'][idxs] = int(4) idxs = (700000<=x_idx) & (x_idx<800000) test['group'][idxs] = int(4) return test[['group']] class permutation_importance(): def __init__(self, model, metric): self.is_computed = False self.n_feat = 0 self.base_score = 0 self.model = model self.metric = metric self.df_result = [] def compute(self, X_valid, y_valid): self.n_feat = len(X_valid.columns) if self.metric == 'auc': y_valid_score = self.model.predict_proba(X_valid)[:, 1] fpr, tpr, thresholds = roc_curve(y_valid, y_valid_score) self.base_score = auc(fpr, tpr) else: pred = np.round(self.model.predict(X_valid)).astype('int8') self.base_score = self.metric(y_valid, pred) self.df_result = pd.DataFrame({'feat': X_valid.columns, 'score': np.zeros(self.n_feat), 'score_diff': np.zeros(self.n_feat)}) # predict for i, col in enumerate(X_valid.columns): df_perm = X_valid.copy() np.random.seed(1) df_perm[col] = np.random.permutation(df_perm[col]) y_valid_pred = self.model.predict(df_perm) if self.metric == 'auc': y_valid_score = self.model.predict_proba(df_perm)[:, 1] fpr, tpr, thresholds = roc_curve(y_valid, y_valid_score) score = auc(fpr, tpr) else: score = self.metric(y_valid, np.round(y_valid_pred).astype('int8')) self.df_result['score'][self.df_result['feat']==col] = score self.df_result['score_diff'][self.df_result['feat']==col] = self.base_score - score self.is_computed = True def get_negative_feature(self): assert self.is_computed!=False, 'compute メソッドが実行されていません' idx = self.df_result['score_diff'] < 0 return self.df_result.loc[idx, 'feat'].values.tolist() def get_positive_feature(self): assert self.is_computed!=False, 'compute メソッドが実行されていません' idx = self.df_result['score_diff'] > 0 return self.df_result.loc[idx, 'feat'].values.tolist() def show_permutation_importance(self, score_type='loss'): '''score_type = 'loss' or 'accuracy' ''' assert self.is_computed!=False, 'compute メソッドが実行されていません' if score_type=='loss': ascending = True elif score_type=='accuracy': ascending = False else: ascending = '' plt.figure(figsize=(15, int(0.25self.n_feat))) sns.barplot(x="score_diff", y="feat", data=self.df_result.sort_values(by="score_diff", ascending=ascending)) plt.title('base_score - permutation_score') def plot_corr(df, abs_=False, threshold=0.95): if abs_==True: corr = df.corr().abs()>threshold vmin = 0 else: corr = df.corr() vmin = -1 # Plot fig, ax = plt.subplots(figsize=(12, 10), dpi=100) fig.patch.set_facecolor('white') sns.heatmap(corr, xticklabels=df.corr().columns, yticklabels=df.corr().columns, vmin=vmin, vmax=1, center=0, annot=False) # Decorations ax.set_title('Correlation', fontsize=22) def get_low_corr_column(df, threshold): df_corr = df.corr() df_corr = abs(df_corr) columns = df_corr.columns # 対角線の値を0にする for i in range(0, len(columns)): df_corr.iloc[i, i] = 0 while True: columns = df_corr.columns max_corr = 0.0 query_column = None target_column = None df_max_column_value = df_corr.max() max_corr = df_max_column_value.max() query_column = df_max_column_value.idxmax() target_column = df_corr[query_column].idxmax() if max_corr < threshold: # しきい値を超えるものがなかったため終了 break else: # しきい値を超えるものがあった場合 delete_column = None saved_column = None # その他との相関の絶対値が大きい方を除去 if sum(df_corr[query_column]) <= sum(df_corr[target_column]): delete_column = target_column saved_column = query_column else: delete_column = query_column saved_column = target_column # 除去すべき特徴を相関行列から消す（行、列） df_corr.drop([delete_column], axis=0, inplace=True) df_corr.drop([delete_column], axis=1, inplace=True) return df_corr.columns # 相関が高い特徴量を除いた名前リスト def reduce_mem_usage(df, verbose=True): numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] start_mem = df.memory_usage().sum() / 10242 for col in df.columns: if col!='open_channels': col_type = df[col].dtypes if col_type in numerics: c_min = df[col].min() c_max = df[col].max() if str(col_type)[:3] == 'int': if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max: df[col] = df[col].astype(np.int8) elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max: df[col] = df[col].astype(np.int16) elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max: df[col] = df[col].astype(np.int32) elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max: df[col] = df[col].astype(np.int64) else: if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max: df[col] = df[col].astype(np.float16) elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max: df[col] = df[col].astype(np.float32) else: df[col] = df[col].astype(np.float64) end_mem = df.memory_usage().sum() / 10242 if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 (start_mem - end_mem) / start_mem)) return df def train_lgbm(X, y, X_te, lgbm_params, random_state=5, n_fold=5, verbose=50, early_stopping_rounds=100, show_fig=True): # using features print(f'features({len(X.columns)}): \n{X.columns}') if not verbose==0 else None # folds = KFold(n_splits=n_fold, shuffle=True, random_state=random_state) folds = StratifiedKFold(n_splits=n_fold, shuffle=True, random_state=random_state) scores = [] oof = np.zeros(len(X)) oof_round = np.zeros(len(X)) test_pred = np.zeros(len(X_te)) df_pi = pd.DataFrame(columns=['feat', 'score_diff']) for fold_n, (train_idx, valid_idx) in enumerate(folds.split(X, y=y)): if verbose==0: pass else: print('\n------------------') print(f'- Fold {fold_n + 1}/{N_FOLD} started at {time.ctime()}') # prepare dataset X_train, X_valid = X.iloc[train_idx], X.iloc[valid_idx] y_train, y_valid = y[train_idx], y[valid_idx] # train model = LGBMRegressor(*lgbm_params, n_estimators=N_ESTIMATORS) model.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_valid, y_valid)], verbose=verbose, early_stopping_rounds=early_stopping_rounds) # pred y_valid_pred = model.predict(X_valid, model.best_iteration_) y_valid_pred_round = np.round(y_valid_pred).astype('int8') _test_pred = model.predict(X_te, model.best_iteration_) if show_fig==False: pass else: # permutation importance pi = permutation_importance(model, f1_macro) # model と metric を渡す pi.compute(X_valid, y_valid) pi_result = pi.df_result df_pi = pd.concat([df_pi, pi_result[['feat', 'score_diff']]]) # result oof[valid_idx] = y_valid_pred oof_round[valid_idx] = y_valid_pred_round score = f1_score(y_valid, y_valid_pred_round, average='macro') scores.append(score) test_pred += _test_pred if verbose==0: pass else: print(f'---> f1-score(macro) valid: {f1_score(y_valid, y_valid_pred_round, average="macro"):.4f}') print('') print('====== finish ======') print('score list:', scores) print('CV mean score(f1_macro): {0:.4f}, std: {1:.4f}'.format(np.mean(scores), np.std(scores))) print(f'oof score(f1_macro): {f1_score(y, oof_round, average="macro"):.4f}') print('') if show_fig==False: pass else: # visualization plt.figure(figsize=(5, 5)) plt.plot([0, 10], [0, 10], color='gray') plt.scatter(y, oof, alpha=0.05, color=cp[1]) plt.xlabel('true') plt.ylabel('pred') plt.show() # confusion_matrix plot_confusion_matrix(y, oof_round, classes=np.arange(11)) # permutation importance plt.figure(figsize=(15, int(0.25len(X.columns)))) order = df_pi.groupby(["feat"]).mean()['score_diff'].reset_index().sort_values('score_diff', ascending=False) sns.barplot(x="score_diff", y="feat", data=df_pi, order=order['feat']) plt.title('base_score - permutation_score') plt.show() # submission test_pred = test_pred/N_FOLD test_pred_round = np.round(test_pred).astype('int8') return test_pred_round, test_pred, oof_round, oof def plot_confusion_matrix(truth, pred, classes, normalize=False, title=''): cm = confusion_matrix(truth, pred) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] plt.figure(figsize=(10, 10)) plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues) plt.title('Confusion matrix', size=15) plt.colorbar(fraction=0.046, pad=0.04) tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) fmt = '.2f' if normalize else 'd' thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.ylabel('True label') plt.xlabel('Predicted label') plt.grid(False) plt.tight_layout() def train_test_split_lgbm(X, y, X_te, lgbm_params, random_state=5, test_size=0.3, verbose=50, early_stopping_rounds=100, show_fig=True): # using features print(f'features({len(X.columns)}): \n{X.columns}') if not verbose==0 else None # folds = KFold(n_splits=n_fold, shuffle=True, random_state=random_state) # folds = StratifiedKFold(n_splits=n_fold, shuffle=True, random_state=random_state) # prepare dataset X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=test_size, random_state=random_state) # train model = LGBMRegressor(**lgbm_params, n_estimators=N_ESTIMATORS) model.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_valid, y_valid)], verbose=verbose, early_stopping_rounds=early_stopping_rounds) # pred oof = model.predict(X_valid, model.best_iteration_) oof_round = np.round(oof).astype('int8') test_pred = model.predict(X_te, model.best_iteration_) test_pred_round = np.round(test_pred).astype('int8') print('====== finish ======') print(f'oof score(f1_macro): {f1_score(y_valid, oof_round, average="macro"):.4f}') print('') if show_fig==False: pass else: # visualization plt.figure(figsize=(5, 5)) plt.plot([0, 10], [0, 10], color='gray') plt.scatter(y_valid, oof, alpha=0.05, color=cp[1]) plt.xlabel('true') plt.ylabel('pred') plt.show() # confusion_matrix plot_confusion_matrix(y_valid, oof_round, classes=np.arange(11)) # permutation importance pi = permutation_importance(model, f1_macro) # model と metric を渡す pi.compute(X_valid, y_valid) pi.show_permutation_importance(score_type='accuracy') # loss or accuracy plt.show() return test_pred_round, test_pred, oof_round, oof	_____no_output_____	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
ref: https://www.kaggle.com/martxelo/fe-and-ensemble-mlp-and-lgbm	def calc_gradients(s, n_grads=4): ''' Calculate gradients for a pandas series. Returns the same number of samples ''' grads = pd.DataFrame() g = s.values for i in range(n_grads): g = np.gradient(g) grads['grad_' + str(i+1)] = g return grads def calc_low_pass(s, n_filts=10): ''' Applies low pass filters to the signal. Left delayed and no delayed ''' wns = np.logspace(-2, -0.3, n_filts) # wns = [0.3244] low_pass = pd.DataFrame() x = s.values for wn in wns: b, a = signal.butter(1, Wn=wn, btype='low') zi = signal.lfilter_zi(b, a) low_pass['lowpass_lf_' + str('%.4f' %wn)] = signal.lfilter(b, a, x, zi=zix[0])[0] low_pass['lowpass_ff_' + str('%.4f' %wn)] = signal.filtfilt(b, a, x) return low_pass def calc_high_pass(s, n_filts=10): ''' Applies high pass filters to the signal. Left delayed and no delayed ''' wns = np.logspace(-2, -0.1, n_filts) # wns = [0.0100, 0.0264, 0.0699, 0.3005, 0.4885, 0.7943] high_pass = pd.DataFrame() x = s.values for wn in wns: b, a = signal.butter(1, Wn=wn, btype='high') zi = signal.lfilter_zi(b, a) high_pass['highpass_lf_' + str('%.4f' %wn)] = signal.lfilter(b, a, x, zi=zix[0])[0] high_pass['highpass_ff_' + str('%.4f' %wn)] = signal.filtfilt(b, a, x) return high_pass def calc_roll_stats(s, windows=[10, 50, 100, 500, 1000, 3000]): ''' Calculates rolling stats like mean, std, min, max... ''' roll_stats = pd.DataFrame() for w in windows: roll_stats['roll_mean_' + str(w)] = s.rolling(window=w, min_periods=1).mean().interpolate('spline', order=5, limit_direction='both') roll_stats['roll_std_' + str(w)] = s.rolling(window=w, min_periods=1).std().interpolate('spline', order=5, limit_direction='both') roll_stats['roll_min_' + str(w)] = s.rolling(window=w, min_periods=1).min().interpolate('spline', order=5, limit_direction='both') roll_stats['roll_max_' + str(w)] = s.rolling(window=w, min_periods=1).max().interpolate('spline', order=5, limit_direction='both') roll_stats['roll_range_' + str(w)] = roll_stats['roll_max_' + str(w)] - roll_stats['roll_min_' + str(w)] roll_stats['roll_q10_' + str(w)] = s.rolling(window=w, min_periods=1).quantile(0.10).interpolate('spline', order=5, limit_direction='both') roll_stats['roll_q25_' + str(w)] = s.rolling(window=w, min_periods=1).quantile(0.25).interpolate('spline', order=5, limit_direction='both') roll_stats['roll_q50_' + str(w)] = s.rolling(window=w, min_periods=1).quantile(0.50).interpolate('spline', order=5, limit_direction='both') roll_stats['roll_q75_' + str(w)] = s.rolling(window=w, min_periods=1).quantile(0.75).interpolate('spline', order=5, limit_direction='both') roll_stats['roll_q90_' + str(w)] = s.rolling(window=w, min_periods=1).quantile(0.90).interpolate('spline', order=5, limit_direction='both') # add zeros when na values (std) # roll_stats = roll_stats.fillna(value=0) return roll_stats def calc_ewm(s, windows=[10, 50, 100, 500, 1000, 3000]): ''' Calculates exponential weighted functions ''' ewm = pd.DataFrame() for w in windows: ewm['ewm_mean_' + str(w)] = s.ewm(span=w, min_periods=1).mean() ewm['ewm_std_' + str(w)] = s.ewm(span=w, min_periods=1).std() # add zeros when na values (std) ewm = ewm.fillna(value=0) return ewm def divide_and_add_features(s, signal_size=500000): ''' Divide the signal in bags of "signal_size". Normalize the data dividing it by 15.0 ''' # normalize s = s/15.0 ls = [] for i in progress_bar(range(int(s.shape[0]/signal_size))): sig = s[isignal_size:(i+1)signal_size].copy().reset_index(drop=True) sig_featured = add_features(sig) ls.append(sig_featured) return pd.concat(ls, axis=0)	_____no_output_____	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
ref: https://www.kaggle.com/nxrprime/single-model-lgbm-kalman-filter-ii	def Kalman1D(observations,damping=1): # To return the smoothed time series data observation_covariance = damping initial_value_guess = observations[0] transition_matrix = 1 transition_covariance = 0.1 initial_value_guess kf = KalmanFilter( initial_state_mean=initial_value_guess, initial_state_covariance=observation_covariance, observation_covariance=observation_covariance, transition_covariance=transition_covariance, transition_matrices=transition_matrix ) pred_state, state_cov = kf.smooth(observations) return pred_state	_____no_output_____	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
Preparation setting	sns.set()	_____no_output_____	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
load dataset	df_tr = pd.read_csv(PATH_TRAIN) df_te = pd.read_csv(PATH_TEST)	_____no_output_____	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
処理のしやすさのために、バッチ番号を振る	batch_list = [] for n in range(10): batchs = np.ones(500000)n batch_list.append(batchs.astype(int)) batch_list = np.hstack(batch_list) df_tr['batch'] = batch_list batch_list = [] for n in range(4): batchs = np.ones(500000)n batch_list.append(batchs.astype(int)) batch_list = np.hstack(batch_list) df_te['batch'] = batch_list	_____no_output_____	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
smallset?	if isSmallSet: print('small set mode') # train batchs = df_tr['batch'].values dfs = [] for i_bt, bt in enumerate(df_tr['batch'].unique()): idxs = batchs == bt _df = df_tr[idxs][:LENGTH].copy() dfs.append(_df) df_tr = pd.concat(dfs).reset_index(drop=True) # test batchs = df_te['batch'].values dfs = [] for i_bt, bt in enumerate(df_te['batch'].unique()): idxs = batchs == bt _df = df_te[idxs][:LENGTH].copy() dfs.append(_df) df_te = pd.concat(dfs).reset_index(drop=True)	_____no_output_____	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
Train	# configurations and main hyperparammeters # EPOCHS = 180 EPOCHS = 180 NNBATCHSIZE = 16 GROUP_BATCH_SIZE = 4000 SEED = 321 LR = 0.0015 SPLITS = 6 def seed_everything(seed): random.seed(seed) np.random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) # tf.random.set_seed(seed) # read data def read_data(): train = pd.read_csv(PATH_TRAIN, dtype={'time': np.float32, 'signal': np.float32, 'open_channels':np.int32}) test = pd.read_csv(PATH_TEST, dtype={'time': np.float32, 'signal': np.float32}) sub = pd.read_csv(PATH_SMPLE_SUB, dtype={'time': np.float32}) # Y_train_proba = np.load('./../data/input/Y_train_proba.npy') # Y_test_proba = np.load('./../data/input/Y_test_proba.npy') probas = np.load('./../data/output_ignore/probas_nb034_RandomForestClassifier_cv_0.9383.npz') Y_train_proba = probas['arr_0'] Y_test_proba = probas['arr_1'] for i in range(11): train[f"proba_{i}"] = Y_train_proba[:, i] test[f"proba_{i}"] = Y_test_proba[:, i] return train, test, sub # create batches of 4000 observations def batching(df, batch_size): df['group'] = df.groupby(df.index//batch_size, sort=False)['signal'].agg(['ngroup']).values df['group'] = df['group'].astype(np.uint16) return df # normalize the data (standard scaler). We can also try other scalers for a better score! def normalize(train, test): train_input_mean = train.signal.mean() train_input_sigma = train.signal.std() train['signal'] = (train.signal - train_input_mean) / train_input_sigma test['signal'] = (test.signal - train_input_mean) / train_input_sigma return train, test # get lead and lags features def lag_with_pct_change(df, windows): for window in windows: df['signal_shift_pos_' + str(window)] = df.groupby('group')['signal'].shift(window).fillna(0) df['signal_shift_neg_' + str(window)] = df.groupby('group')['signal'].shift(-1 * window).fillna(0) return df # main module to run feature engineering. Here you may want to try and add other features and check if your score imporves :). def run_feat_engineering(df, batch_size): # create batches df = batching(df, batch_size = batch_size) # create leads and lags (1, 2, 3 making them 6 features) df = lag_with_pct_change(df, [1, 2, 3]) # create signal 2 (this is the new feature) df['signal_2'] = df['signal'] 2 return df # fillna with the mean and select features for training def feature_selection(train, test): features = [col for col in train.columns if col not in ['index', 'group', 'open_channels', 'time']] train = train.replace([np.inf, -np.inf], np.nan) test = test.replace([np.inf, -np.inf], np.nan) for feature in features: feature_mean = pd.concat([train[feature], test[feature]], axis = 0).mean() train[feature] = train[feature].fillna(feature_mean) test[feature] = test[feature].fillna(feature_mean) return train, test, features # model function (very important, you can try different arquitectures to get a better score. I believe that top public leaderboard is a 1D Conv + RNN style) def Classifier(shape_): def cbr(x, out_layer, kernel, stride, dilation): x = Conv1D(out_layer, kernel_size=kernel, dilation_rate=dilation, strides=stride, padding="same")(x) x = BatchNormalization()(x) x = Activation("relu")(x) return x def wave_block(x, filters, kernel_size, n): dilation_rates = [2**i for i in range(n)] x = Conv1D(filters = filters, kernel_size = 1, padding = 'same')(x) res_x = x for dilation_rate in dilation_rates: tanh_out = Conv1D(filters = filters, kernel_size = kernel_size, padding = 'same', activation = 'tanh', dilation_rate = dilation_rate)(x) sigm_out = Conv1D(filters = filters, kernel_size = kernel_size, padding = 'same', activation = 'sigmoid', dilation_rate = dilation_rate)(x) x = Multiply()([tanh_out, sigm_out]) x = Conv1D(filters = filters, kernel_size = 1, padding = 'same')(x) res_x = Add()([res_x, x]) return res_x inp = Input(shape = (shape_)) x = cbr(inp, 64, 7, 1, 1) x = BatchNormalization()(x) x = wave_block(x, 16, 3, 12) x = BatchNormalization()(x) x = wave_block(x, 32, 3, 8) x = BatchNormalization()(x) x = wave_block(x, 64, 3, 4) x = BatchNormalization()(x) x = wave_block(x, 128, 3, 1) x = cbr(x, 32, 7, 1, 1) x = BatchNormalization()(x) x = Dropout(0.2)(x) out = Dense(11, activation = 'softmax', name = 'out')(x) model = models.Model(inputs = inp, outputs = out) opt = Adam(lr = LR) # opt = tfa.optimizers.SWA(opt) # model.compile(loss = losses.CategoricalCrossentropy(), optimizer = opt, metrics = ['accuracy']) model.compile(loss = categorical_crossentropy, optimizer = opt, metrics = ['accuracy']) return model # function that decrease the learning as epochs increase (i also change this part of the code) def lr_schedule(epoch): if epoch < 30: lr = LR elif epoch < 40: lr = LR / 3 elif epoch < 50: lr = LR / 5 elif epoch < 60: lr = LR / 7 elif epoch < 70: lr = LR / 9 elif epoch < 80: lr = LR / 11 elif epoch < 90: lr = LR / 13 else: lr = LR / 100 return lr # class to get macro f1 score. This is not entirely necessary but it's fun to check f1 score of each epoch (be carefull, if you use this function early stopping callback will not work) class MacroF1(Callback): def __init__(self, model, inputs, targets): self.model = model self.inputs = inputs self.targets = np.argmax(targets, axis = 2).reshape(-1) def on_epoch_end(self, epoch, logs): pred = np.argmax(self.model.predict(self.inputs), axis = 2).reshape(-1) score = f1_score(self.targets, pred, average = 'macro') print(f'F1 Macro Score: {score:.5f}') # main function to perfrom groupkfold cross validation (we have 1000 vectores of 4000 rows and 8 features (columns)). Going to make 5 groups with this subgroups. def run_cv_model_by_batch(train, test, splits, batch_col, feats, sample_submission, nn_epochs, nn_batch_size): seed_everything(SEED) K.clear_session() # config = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1, # gpu_options=tf.compat.v1.GPUOptions( # visible_device_list='4', # specify GPU number # allow_growth=True # ) # ) # sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=config) # tf.compat.v1.keras.backend.set_session(sess) # tf.compat.v1 ---> tf (tensorflow2系からtensorflow1系に変更) config = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1, # gpu_options=tf.GPUOptions( # visible_device_list='4', # specify GPU number # allow_growth=True # ) ) sess = tf.Session(graph=tf.get_default_graph(), config=config) tf.keras.backend.set_session(sess) oof_ = np.zeros((len(train), 11)) # build out of folds matrix with 11 columns, they represent our target variables classes (from 0 to 10) preds_ = np.zeros((len(test), 11)) target = ['open_channels'] group = train['group'] kf = GroupKFold(n_splits=5) splits = [x for x in kf.split(train, train[target], group)] new_splits = [] for sp in splits: new_split = [] new_split.append(np.unique(group[sp[0]])) new_split.append(np.unique(group[sp[1]])) new_split.append(sp[1]) new_splits.append(new_split) # pivot target columns to transform the net to a multiclass classification estructure (you can also leave it in 1 vector with sparsecategoricalcrossentropy loss function) tr = pd.concat([pd.get_dummies(train.open_channels), train[['group']]], axis=1) tr.columns = ['target_'+str(i) for i in range(11)] + ['group'] target_cols = ['target_'+str(i) for i in range(11)] train_tr = np.array(list(tr.groupby('group').apply(lambda x: x[target_cols].values))).astype(np.float32) train = np.array(list(train.groupby('group').apply(lambda x: x[feats].values))) test = np.array(list(test.groupby('group').apply(lambda x: x[feats].values))) for n_fold, (tr_idx, val_idx, val_orig_idx) in enumerate(new_splits[0:], start=0): train_x, train_y = train[tr_idx], train_tr[tr_idx] valid_x, valid_y = train[val_idx], train_tr[val_idx] print(f'Our training dataset shape is {train_x.shape}') print(f'Our validation dataset shape is {valid_x.shape}') gc.collect() shape_ = (None, train_x.shape[2]) # input is going to be the number of feature we are using (dimension 2 of 0, 1, 2) model = Classifier(shape_) # using our lr_schedule function cb_lr_schedule = LearningRateScheduler(lr_schedule) model.fit(train_x,train_y, epochs = nn_epochs, callbacks = [cb_lr_schedule, MacroF1(model, valid_x, valid_y)], # adding custom evaluation metric for each epoch batch_size = nn_batch_size,verbose = 2, validation_data = (valid_x,valid_y)) preds_f = model.predict(valid_x) f1_score_ = f1_score(np.argmax(valid_y, axis=2).reshape(-1), np.argmax(preds_f, axis=2).reshape(-1), average = 'macro') # need to get the class with the biggest probability print(f'Training fold {n_fold + 1} completed. macro f1 score : {f1_score_ :1.5f}') preds_f = preds_f.reshape(-1, preds_f.shape[-1]) oof_[val_orig_idx,:] += preds_f te_preds = model.predict(test) te_preds = te_preds.reshape(-1, te_preds.shape[-1]) preds_ += te_preds / SPLITS # calculate the oof macro f1_score f1_score_ = f1_score(np.argmax(train_tr, axis = 2).reshape(-1), np.argmax(oof_, axis = 1), average = 'macro') # axis 2 for the 3 Dimension array and axis 1 for the 2 Domension Array (extracting the best class) print(f'Training completed. oof macro f1 score : {f1_score_:1.5f}') save_path = f'{DIR_OUTPUT}submission_nb{NB}_cv_{f1_score_:.4f}.csv' print(f'save path: {save_path}') sample_submission['open_channels'] = np.argmax(preds_, axis = 1).astype(int) sample_submission.to_csv(save_path, index=False, float_format='%.4f') # save_path = f'{DIR_OUTPUT}oof_nb{NB}_cv_{f1_score_:.4f}.csv' # sample_submission['open_channels'] = np.argmax(preds_, axis = 1).astype(int) # sample_submission.to_csv(save_path, index=False, float_format='%.4f') return oof_ %%time # this function run our entire program def run_everything(): print(f'Reading Data Started...({time.ctime()})') train, test, sample_submission = read_data() train, test = normalize(train, test) print(f'Reading and Normalizing Data Completed') print(f'Creating Features({time.ctime()})') print(f'Feature Engineering Started...') train = run_feat_engineering(train, batch_size = GROUP_BATCH_SIZE) test = run_feat_engineering(test, batch_size = GROUP_BATCH_SIZE) train, test, features = feature_selection(train, test) print(f'Feature Engineering Completed...') print(f'Training Wavenet model with {SPLITS} folds of GroupKFold Started...({time.ctime()})') oof_ = run_cv_model_by_batch(train, test, SPLITS, 'group', features, sample_submission, EPOCHS, NNBATCHSIZE) print(f'Training completed...') return oof_ oof_ = run_everything()	Reading Data Started...(Wed May 6 11:15:32 2020) Reading and Normalizing Data Completed Creating Features(Wed May 6 11:15:36 2020) Feature Engineering Started... Feature Engineering Completed... Training Wavenet model with 6 folds of GroupKFold Started...(Wed May 6 11:15:46 2020) Our training dataset shape is (1000, 4000, 19) Our validation dataset shape is (250, 4000, 19) Train on 1000 samples, validate on 250 samples Epoch 1/180 F1 Macro Score: 0.65447 - 23s - loss: 0.6088 - acc: 0.8310 - val_loss: 0.8985 - val_acc: 0.8545 Epoch 2/180 F1 Macro Score: 0.86893 - 8s - loss: 0.1985 - acc: 0.9559 - val_loss: 0.4352 - val_acc: 0.9475 Epoch 3/180 F1 Macro Score: 0.93159 - 9s - loss: 0.1436 - acc: 0.9642 - val_loss: 0.2295 - val_acc: 0.9642 Epoch 4/180 F1 Macro Score: 0.93344 - 8s - loss: 0.1296 - acc: 0.9656 - val_loss: 0.1473 - val_acc: 0.9650 Epoch 5/180 F1 Macro Score: 0.93503 - 9s - loss: 0.1221 - acc: 0.9661 - val_loss: 0.1301 - val_acc: 0.9655 Epoch 6/180 F1 Macro Score: 0.93693 - 9s - loss: 0.1177 - acc: 0.9665 - val_loss: 0.1105 - val_acc: 0.9663 Epoch 7/180 F1 Macro Score: 0.93668 - 9s - loss: 0.1130 - acc: 0.9667 - val_loss: 0.1085 - val_acc: 0.9662 Epoch 8/180 F1 Macro Score: 0.93671 - 9s - loss: 0.1092 - acc: 0.9670 - val_loss: 0.1023 - val_acc: 0.9665 Epoch 9/180 F1 Macro Score: 0.93550 - 9s - loss: 0.1081 - acc: 0.9670 - val_loss: 0.1055 - val_acc: 0.9657 Epoch 10/180 F1 Macro Score: 0.93486 - 9s - loss: 0.1074 - acc: 0.9668 - val_loss: 0.1144 - val_acc: 0.9654 Epoch 11/180 F1 Macro Score: 0.93078 - 9s - loss: 0.1196 - acc: 0.9654 - val_loss: 0.1083 - val_acc: 0.9652 Epoch 12/180 F1 Macro Score: 0.93584 - 9s - loss: 0.1153 - acc: 0.9659 - val_loss: 0.1019 - val_acc: 0.9663 Epoch 13/180 F1 Macro Score: 0.93794 - 9s - loss: 0.1044 - acc: 0.9672 - val_loss: 0.0958 - val_acc: 0.9668 Epoch 14/180 F1 Macro Score: 0.93633 - 9s - loss: 0.1035 - acc: 0.9671 - val_loss: 0.0983 - val_acc: 0.9661 Epoch 15/180 F1 Macro Score: 0.93802 - 8s - loss: 0.1002 - acc: 0.9675 - val_loss: 0.0938 - val_acc: 0.9670 Epoch 16/180 F1 Macro Score: 0.93670 - 9s - loss: 0.1003 - acc: 0.9674 - val_loss: 0.0964 - val_acc: 0.9663 Epoch 17/180 F1 Macro Score: 0.93737 - 9s - loss: 0.0981 - acc: 0.9675 - val_loss: 0.0938 - val_acc: 0.9666 Epoch 18/180 F1 Macro Score: 0.93661 - 8s - loss: 0.0974 - acc: 0.9676 - val_loss: 0.0931 - val_acc: 0.9668 Epoch 19/180 F1 Macro Score: 0.93760 - 8s - loss: 0.0965 - acc: 0.9676 - val_loss: 0.0917 - val_acc: 0.9668 Epoch 20/180 F1 Macro Score: 0.93758 - 8s - loss: 0.0949 - acc: 0.9677 - val_loss: 0.0928 - val_acc: 0.9667 Epoch 21/180 F1 Macro Score: 0.93429 - 8s - loss: 0.0953 - acc: 0.9676 - val_loss: 0.0972 - val_acc: 0.9658 Epoch 22/180 F1 Macro Score: 0.93760 - 8s - loss: 0.0953 - acc: 0.9675 - val_loss: 0.0915 - val_acc: 0.9669 Epoch 23/180 F1 Macro Score: 0.93586 - 8s - loss: 0.0939 - acc: 0.9678 - val_loss: 0.0946 - val_acc: 0.9663 Epoch 24/180 F1 Macro Score: 0.93759 - 9s - loss: 0.0925 - acc: 0.9679 - val_loss: 0.0912 - val_acc: 0.9667 Epoch 25/180 F1 Macro Score: 0.93796 - 8s - loss: 0.0923 - acc: 0.9680 - val_loss: 0.0902 - val_acc: 0.9669 Epoch 26/180 F1 Macro Score: 0.93698 - 9s - loss: 0.0919 - acc: 0.9680 - val_loss: 0.0913 - val_acc: 0.9663 Epoch 27/180 F1 Macro Score: 0.93641 - 9s - loss: 0.0948 - acc: 0.9675 - val_loss: 0.0920 - val_acc: 0.9667 Epoch 28/180 F1 Macro Score: 0.93774 - 9s - loss: 0.0912 - acc: 0.9681 - val_loss: 0.0898 - val_acc: 0.9670 Epoch 29/180 F1 Macro Score: 0.93779 - 7s - loss: 0.0909 - acc: 0.9680 - val_loss: 0.0908 - val_acc: 0.9668 Epoch 30/180 F1 Macro Score: 0.93654 - 8s - loss: 0.0899 - acc: 0.9681 - val_loss: 0.0926 - val_acc: 0.9661 Epoch 31/180 F1 Macro Score: 0.93856 - 7s - loss: 0.0893 - acc: 0.9684 - val_loss: 0.0882 - val_acc: 0.9673 Epoch 32/180 F1 Macro Score: 0.93897 - 9s - loss: 0.0873 - acc: 0.9688 - val_loss: 0.0871 - val_acc: 0.9676 Epoch 33/180 F1 Macro Score: 0.93890 - 8s - loss: 0.0866 - acc: 0.9688 - val_loss: 0.0870 - val_acc: 0.9676 Epoch 34/180 F1 Macro Score: 0.93929 - 8s - loss: 0.0865 - acc: 0.9689 - val_loss: 0.0859 - val_acc: 0.9678 Epoch 35/180 F1 Macro Score: 0.93905 - 8s - loss: 0.0864 - acc: 0.9689 - val_loss: 0.0863 - val_acc: 0.9676 Epoch 36/180 F1 Macro Score: 0.93888 - 8s - loss: 0.0860 - acc: 0.9691 - val_loss: 0.0865 - val_acc: 0.9675 Epoch 37/180 F1 Macro Score: 0.93732 - 9s - loss: 0.0855 - acc: 0.9692 - val_loss: 0.0900 - val_acc: 0.9668 Epoch 38/180 F1 Macro Score: 0.93992 - 8s - loss: 0.0847 - acc: 0.9693 - val_loss: 0.0851 - val_acc: 0.9681 Epoch 39/180 F1 Macro Score: 0.93891 - 8s - loss: 0.0873 - acc: 0.9689 - val_loss: 0.0860 - val_acc: 0.9677 Epoch 40/180 F1 Macro Score: 0.93900 - 9s - loss: 0.0849 - acc: 0.9693 - val_loss: 0.0862 - val_acc: 0.9677 Epoch 41/180 F1 Macro Score: 0.94022 - 8s - loss: 0.0842 - acc: 0.9696 - val_loss: 0.0845 - val_acc: 0.9682 Epoch 42/180 F1 Macro Score: 0.94046 - 9s - loss: 0.0832 - acc: 0.9697 - val_loss: 0.0842 - val_acc: 0.9684 Epoch 43/180 F1 Macro Score: 0.93883 - 9s - loss: 0.0835 - acc: 0.9697 - val_loss: 0.0861 - val_acc: 0.9679 Epoch 44/180 F1 Macro Score: 0.94063 - 9s - loss: 0.0831 - acc: 0.9697 - val_loss: 0.0841 - val_acc: 0.9684 Epoch 45/180 F1 Macro Score: 0.93997 - 8s - loss: 0.0831 - acc: 0.9698 - val_loss: 0.0846 - val_acc: 0.9682 Epoch 46/180 F1 Macro Score: 0.93945 - 7s - loss: 0.0831 - acc: 0.9699 - val_loss: 0.0854 - val_acc: 0.9680 Epoch 47/180 F1 Macro Score: 0.94035 - 8s - loss: 0.0829 - acc: 0.9699 - val_loss: 0.0840 - val_acc: 0.9683 Epoch 48/180 F1 Macro Score: 0.94079 - 8s - loss: 0.0826 - acc: 0.9699 - val_loss: 0.0837 - val_acc: 0.9686 Epoch 49/180 F1 Macro Score: 0.93997 - 9s - loss: 0.0823 - acc: 0.9700 - val_loss: 0.0846 - val_acc: 0.9681 Epoch 50/180 F1 Macro Score: 0.94037 - 7s - loss: 0.0821 - acc: 0.9700 - val_loss: 0.0844 - val_acc: 0.9683 Epoch 51/180 F1 Macro Score: 0.94070 - 8s - loss: 0.0825 - acc: 0.9699 - val_loss: 0.0836 - val_acc: 0.9685 Epoch 52/180 F1 Macro Score: 0.94046 - 9s - loss: 0.0815 - acc: 0.9702 - val_loss: 0.0837 - val_acc: 0.9685 Epoch 53/180 F1 Macro Score: 0.94078 - 9s - loss: 0.0816 - acc: 0.9702 - val_loss: 0.0836 - val_acc: 0.9686 Epoch 54/180 F1 Macro Score: 0.93971 - 9s - loss: 0.0818 - acc: 0.9701 - val_loss: 0.0847 - val_acc: 0.9682 Epoch 55/180 F1 Macro Score: 0.94058 - 9s - loss: 0.0817 - acc: 0.9701 - val_loss: 0.0838 - val_acc: 0.9684 Epoch 56/180 F1 Macro Score: 0.94053 - 8s - loss: 0.0817 - acc: 0.9701 - val_loss: 0.0840 - val_acc: 0.9684 Epoch 57/180 F1 Macro Score: 0.94036 - 9s - loss: 0.0809 - acc: 0.9703 - val_loss: 0.0836 - val_acc: 0.9684 Epoch 58/180 F1 Macro Score: 0.93987 - 9s - loss: 0.0814 - acc: 0.9701 - val_loss: 0.0847 - val_acc: 0.9681 Epoch 59/180 F1 Macro Score: 0.94029 - 9s - loss: 0.0821 - acc: 0.9699 - val_loss: 0.0844 - val_acc: 0.9683 Epoch 60/180 F1 Macro Score: 0.94081 - 9s - loss: 0.0833 - acc: 0.9697 - val_loss: 0.0841 - val_acc: 0.9685 Epoch 61/180 F1 Macro Score: 0.94069 - 8s - loss: 0.0808 - acc: 0.9704 - val_loss: 0.0837 - val_acc: 0.9685 Epoch 62/180 F1 Macro Score: 0.94080 - 9s - loss: 0.0812 - acc: 0.9702 - val_loss: 0.0834 - val_acc: 0.9686 Epoch 63/180 F1 Macro Score: 0.94053 - 8s - loss: 0.0809 - acc: 0.9703 - val_loss: 0.0835 - val_acc: 0.9686 Epoch 64/180 F1 Macro Score: 0.94075 - 9s - loss: 0.0805 - acc: 0.9704 - val_loss: 0.0835 - val_acc: 0.9685 Epoch 65/180 F1 Macro Score: 0.94064 - 8s - loss: 0.0806 - acc: 0.9704 - val_loss: 0.0837 - val_acc: 0.9685 Epoch 66/180 F1 Macro Score: 0.93868 - 9s - loss: 0.0797 - acc: 0.9705 - val_loss: 0.0847 - val_acc: 0.9681 Epoch 67/180 F1 Macro Score: 0.94083 - 8s - loss: 0.0802 - acc: 0.9704 - val_loss: 0.0835 - val_acc: 0.9686 Epoch 68/180 F1 Macro Score: 0.94019 - 9s - loss: 0.0799 - acc: 0.9705 - val_loss: 0.0835 - val_acc: 0.9685 Epoch 69/180 F1 Macro Score: 0.94036 - 9s - loss: 0.0799 - acc: 0.9705 - val_loss: 0.0838 - val_acc: 0.9684 Epoch 70/180 F1 Macro Score: 0.94067 - 8s - loss: 0.0801 - acc: 0.9705 - val_loss: 0.0835 - val_acc: 0.9685 Epoch 71/180 F1 Macro Score: 0.94032 - 9s - loss: 0.0800 - acc: 0.9705 - val_loss: 0.0836 - val_acc: 0.9684 Epoch 72/180 F1 Macro Score: 0.94037 - 9s - loss: 0.0798 - acc: 0.9706 - val_loss: 0.0836 - val_acc: 0.9684 Epoch 73/180 F1 Macro Score: 0.94060 - 8s - loss: 0.0798 - acc: 0.9706 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 74/180 F1 Macro Score: 0.94058 - 9s - loss: 0.0790 - acc: 0.9707 - val_loss: 0.0836 - val_acc: 0.9685 Epoch 75/180 F1 Macro Score: 0.94053 - 9s - loss: 0.0798 - acc: 0.9705 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 76/180 F1 Macro Score: 0.94075 - 9s - loss: 0.0791 - acc: 0.9707 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 77/180 F1 Macro Score: 0.94047 - 8s - loss: 0.0792 - acc: 0.9706 - val_loss: 0.0837 - val_acc: 0.9684 Epoch 78/180 F1 Macro Score: 0.94027 - 9s - loss: 0.0790 - acc: 0.9708 - val_loss: 0.0836 - val_acc: 0.9683 Epoch 79/180 F1 Macro Score: 0.94051 - 9s - loss: 0.0791 - acc: 0.9706 - val_loss: 0.0836 - val_acc: 0.9684 Epoch 80/180 F1 Macro Score: 0.94027 - 9s - loss: 0.0790 - acc: 0.9707 - val_loss: 0.0839 - val_acc: 0.9683 Epoch 81/180 F1 Macro Score: 0.94077 - 8s - loss: 0.0783 - acc: 0.9708 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 82/180 F1 Macro Score: 0.94056 - 9s - loss: 0.0792 - acc: 0.9707 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 83/180 F1 Macro Score: 0.94059 - 8s - loss: 0.0785 - acc: 0.9708 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 84/180 F1 Macro Score: 0.94060 - 9s - loss: 0.0788 - acc: 0.9708 - val_loss: 0.0837 - val_acc: 0.9684 Epoch 85/180 F1 Macro Score: 0.94033 - 9s - loss: 0.0785 - acc: 0.9708 - val_loss: 0.0839 - val_acc: 0.9683 Epoch 86/180 F1 Macro Score: 0.94054 - 8s - loss: 0.0787 - acc: 0.9708 - val_loss: 0.0836 - val_acc: 0.9684 Epoch 87/180 F1 Macro Score: 0.94048 - 8s - loss: 0.0789 - acc: 0.9707 - val_loss: 0.0839 - val_acc: 0.9682 Epoch 88/180 F1 Macro Score: 0.94024 - 9s - loss: 0.0784 - acc: 0.9709 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 89/180 F1 Macro Score: 0.94008 - 9s - loss: 0.0781 - acc: 0.9708 - val_loss: 0.0840 - val_acc: 0.9682 Epoch 90/180 F1 Macro Score: 0.94065 - 7s - loss: 0.0782 - acc: 0.9709 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 91/180 F1 Macro Score: 0.94085 - 8s - loss: 0.0779 - acc: 0.9711 - val_loss: 0.0832 - val_acc: 0.9685 Epoch 92/180 F1 Macro Score: 0.94063 - 8s - loss: 0.0776 - acc: 0.9711 - val_loss: 0.0832 - val_acc: 0.9685 Epoch 93/180 F1 Macro Score: 0.94070 - 8s - loss: 0.0775 - acc: 0.9712 - val_loss: 0.0832 - val_acc: 0.9685 Epoch 94/180 F1 Macro Score: 0.94071 - 9s - loss: 0.0779 - acc: 0.9711 - val_loss: 0.0832 - val_acc: 0.9685 Epoch 95/180 F1 Macro Score: 0.94068 - 9s - loss: 0.0776 - acc: 0.9711 - val_loss: 0.0832 - val_acc: 0.9685 Epoch 96/180 F1 Macro Score: 0.94070 - 9s - loss: 0.0775 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9686 Epoch 97/180 F1 Macro Score: 0.94063 - 9s - loss: 0.0773 - acc: 0.9712 - val_loss: 0.0832 - val_acc: 0.9685 Epoch 98/180 F1 Macro Score: 0.94082 - 9s - loss: 0.0778 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9686 Epoch 99/180 F1 Macro Score: 0.94078 - 8s - loss: 0.0772 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 100/180 F1 Macro Score: 0.94085 - 9s - loss: 0.0779 - acc: 0.9710 - val_loss: 0.0832 - val_acc: 0.9685 Epoch 101/180 F1 Macro Score: 0.94062 - 8s - loss: 0.0777 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 102/180 F1 Macro Score: 0.94061 - 9s - loss: 0.0777 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9684 Epoch 103/180 F1 Macro Score: 0.94059 - 8s - loss: 0.0776 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 104/180 F1 Macro Score: 0.94073 - 9s - loss: 0.0775 - acc: 0.9711 - val_loss: 0.0832 - val_acc: 0.9685 Epoch 105/180 F1 Macro Score: 0.94066 - 9s - loss: 0.0773 - acc: 0.9712 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 106/180 F1 Macro Score: 0.94070 - 9s - loss: 0.0776 - acc: 0.9711 - val_loss: 0.0832 - val_acc: 0.9685 Epoch 107/180 F1 Macro Score: 0.94068 - 9s - loss: 0.0774 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 108/180 F1 Macro Score: 0.94058 - 9s - loss: 0.0774 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9684 Epoch 109/180 F1 Macro Score: 0.94079 - 9s - loss: 0.0773 - acc: 0.9712 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 110/180 F1 Macro Score: 0.94069 - 9s - loss: 0.0775 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 111/180 F1 Macro Score: 0.94061 - 9s - loss: 0.0775 - acc: 0.9712 - val_loss: 0.0833 - val_acc: 0.9684 Epoch 112/180 F1 Macro Score: 0.94073 - 9s - loss: 0.0776 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 113/180 F1 Macro Score: 0.94052 - 8s - loss: 0.0770 - acc: 0.9712 - val_loss: 0.0833 - val_acc: 0.9684 Epoch 114/180 F1 Macro Score: 0.94066 - 9s - loss: 0.0774 - acc: 0.9711 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 115/180 F1 Macro Score: 0.94057 - 7s - loss: 0.0776 - acc: 0.9710 - val_loss: 0.0833 - val_acc: 0.9684 Epoch 116/180 F1 Macro Score: 0.94074 - 7s - loss: 0.0773 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 117/180 F1 Macro Score: 0.94060 - 7s - loss: 0.0775 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 118/180 F1 Macro Score: 0.94070 - 7s - loss: 0.0771 - acc: 0.9712 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 119/180 F1 Macro Score: 0.94060 - 7s - loss: 0.0776 - acc: 0.9711 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 120/180 F1 Macro Score: 0.94052 - 7s - loss: 0.0769 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 121/180 F1 Macro Score: 0.94071 - 7s - loss: 0.0775 - acc: 0.9712 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 122/180 F1 Macro Score: 0.94047 - 7s - loss: 0.0770 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 123/180 F1 Macro Score: 0.94076 - 7s - loss: 0.0777 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 124/180 F1 Macro Score: 0.94069 - 7s - loss: 0.0774 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 125/180 F1 Macro Score: 0.94077 - 7s - loss: 0.0774 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 126/180 F1 Macro Score: 0.94069 - 7s - loss: 0.0775 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 127/180 F1 Macro Score: 0.94083 - 7s - loss: 0.0774 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 128/180 F1 Macro Score: 0.94051 - 7s - loss: 0.0773 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 129/180 F1 Macro Score: 0.94071 - 7s - loss: 0.0774 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 130/180 F1 Macro Score: 0.94071 - 7s - loss: 0.0772 - acc: 0.9712 - val_loss: 0.0833 - val_acc: 0.9684 Epoch 131/180 F1 Macro Score: 0.94064 - 7s - loss: 0.0771 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 132/180 F1 Macro Score: 0.94044 - 7s - loss: 0.0772 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 133/180 F1 Macro Score: 0.94075 - 7s - loss: 0.0771 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 134/180 F1 Macro Score: 0.94061 - 7s - loss: 0.0770 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 135/180 F1 Macro Score: 0.94037 - 7s - loss: 0.0775 - acc: 0.9712 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 136/180 F1 Macro Score: 0.94069 - 7s - loss: 0.0772 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 137/180 F1 Macro Score: 0.94017 - 7s - loss: 0.0770 - acc: 0.9712 - val_loss: 0.0835 - val_acc: 0.9683 Epoch 138/180 F1 Macro Score: 0.94080 - 7s - loss: 0.0772 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 139/180 F1 Macro Score: 0.94065 - 7s - loss: 0.0773 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 140/180 F1 Macro Score: 0.94053 - 7s - loss: 0.0771 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 141/180 F1 Macro Score: 0.94074 - 7s - loss: 0.0770 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 142/180 F1 Macro Score: 0.94064 - 7s - loss: 0.0769 - acc: 0.9713 - val_loss: 0.0833 - val_acc: 0.9685 Epoch 143/180 F1 Macro Score: 0.94073 - 7s - loss: 0.0771 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 144/180 F1 Macro Score: 0.94055 - 7s - loss: 0.0770 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 145/180 F1 Macro Score: 0.94076 - 7s - loss: 0.0773 - acc: 0.9711 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 146/180 F1 Macro Score: 0.94054 - 7s - loss: 0.0770 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 147/180 F1 Macro Score: 0.94072 - 7s - loss: 0.0771 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 148/180 F1 Macro Score: 0.94065 - 7s - loss: 0.0771 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 149/180 F1 Macro Score: 0.94073 - 7s - loss: 0.0773 - acc: 0.9712 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 150/180 F1 Macro Score: 0.94070 - 7s - loss: 0.0769 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 151/180 F1 Macro Score: 0.94068 - 7s - loss: 0.0769 - acc: 0.9713 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 152/180 F1 Macro Score: 0.94055 - 7s - loss: 0.0769 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 153/180 F1 Macro Score: 0.94060 - 7s - loss: 0.0772 - acc: 0.9712 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 154/180 F1 Macro Score: 0.94058 - 8s - loss: 0.0768 - acc: 0.9713 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 155/180 F1 Macro Score: 0.94071 - 8s - loss: 0.0773 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 156/180 F1 Macro Score: 0.94062 - 7s - loss: 0.0770 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 157/180 F1 Macro Score: 0.94070 - 7s - loss: 0.0772 - acc: 0.9712 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 158/180 F1 Macro Score: 0.94057 - 7s - loss: 0.0769 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 159/180 F1 Macro Score: 0.94049 - 7s - loss: 0.0766 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 160/180 F1 Macro Score: 0.94061 - 7s - loss: 0.0767 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 161/180 F1 Macro Score: 0.94060 - 7s - loss: 0.0767 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 162/180 F1 Macro Score: 0.94046 - 7s - loss: 0.0772 - acc: 0.9712 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 163/180 F1 Macro Score: 0.94053 - 7s - loss: 0.0769 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 164/180 F1 Macro Score: 0.94036 - 7s - loss: 0.0768 - acc: 0.9712 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 165/180 F1 Macro Score: 0.94063 - 7s - loss: 0.0767 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 166/180 F1 Macro Score: 0.94062 - 7s - loss: 0.0766 - acc: 0.9714 - val_loss: 0.0834 - val_acc: 0.9684 Epoch 167/180 F1 Macro Score: 0.94027 - 7s - loss: 0.0767 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9683 Epoch 168/180 F1 Macro Score: 0.94071 - 7s - loss: 0.0769 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9685 Epoch 169/180 F1 Macro Score: 0.94061 - 7s - loss: 0.0767 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 170/180 F1 Macro Score: 0.94066 - 7s - loss: 0.0767 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9685 Epoch 171/180 F1 Macro Score: 0.94061 - 7s - loss: 0.0775 - acc: 0.9712 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 172/180 F1 Macro Score: 0.94064 - 7s - loss: 0.0766 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 173/180 F1 Macro Score: 0.94058 - 7s - loss: 0.0767 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 174/180 F1 Macro Score: 0.94054 - 7s - loss: 0.0768 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 175/180 F1 Macro Score: 0.94059 - 7s - loss: 0.0767 - acc: 0.9715 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 176/180 F1 Macro Score: 0.94069 - 7s - loss: 0.0766 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9685 Epoch 177/180 F1 Macro Score: 0.94052 - 7s - loss: 0.0767 - acc: 0.9712 - val_loss: 0.0837 - val_acc: 0.9684 Epoch 178/180 F1 Macro Score: 0.94053 - 7s - loss: 0.0767 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 179/180 F1 Macro Score: 0.94058 - 7s - loss: 0.0765 - acc: 0.9714 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 180/180 F1 Macro Score: 0.94059 - 7s - loss: 0.0769 - acc: 0.9713 - val_loss: 0.0835 - val_acc: 0.9684 Training fold 1 completed. macro f1 score : 0.94059 Our training dataset shape is (1000, 4000, 19) Our validation dataset shape is (250, 4000, 19) Train on 1000 samples, validate on 250 samples Epoch 1/180 F1 Macro Score: 0.77329 - 18s - loss: 0.5534 - acc: 0.8433 - val_loss: 0.7989 - val_acc: 0.9029 Epoch 2/180 F1 Macro Score: 0.92960 - 7s - loss: 0.1838 - acc: 0.9570 - val_loss: 0.3918 - val_acc: 0.9656 Epoch 3/180 F1 Macro Score: 0.93509 - 7s - loss: 0.1376 - acc: 0.9644 - val_loss: 0.1898 - val_acc: 0.9676 Epoch 4/180 F1 Macro Score: 0.93554 - 7s - loss: 0.1277 - acc: 0.9651 - val_loss: 0.1339 - val_acc: 0.9678 Epoch 5/180 F1 Macro Score: 0.93814 - 7s - loss: 0.1222 - acc: 0.9656 - val_loss: 0.1057 - val_acc: 0.9690 Epoch 6/180 F1 Macro Score: 0.93775 - 7s - loss: 0.1159 - acc: 0.9660 - val_loss: 0.0999 - val_acc: 0.9690 Epoch 7/180 F1 Macro Score: 0.93842 - 7s - loss: 0.1120 - acc: 0.9662 - val_loss: 0.0940 - val_acc: 0.9693 Epoch 8/180 F1 Macro Score: 0.93867 - 7s - loss: 0.1109 - acc: 0.9662 - val_loss: 0.0919 - val_acc: 0.9693 Epoch 9/180 F1 Macro Score: 0.93819 - 7s - loss: 0.1076 - acc: 0.9664 - val_loss: 0.0924 - val_acc: 0.9691 Epoch 10/180 F1 Macro Score: 0.93710 - 7s - loss: 0.1076 - acc: 0.9663 - val_loss: 0.0942 - val_acc: 0.9688 Epoch 11/180 F1 Macro Score: 0.93789 - 7s - loss: 0.1061 - acc: 0.9663 - val_loss: 0.0914 - val_acc: 0.9690 Epoch 12/180 F1 Macro Score: 0.93780 - 8s - loss: 0.1042 - acc: 0.9664 - val_loss: 0.0916 - val_acc: 0.9689 Epoch 13/180 F1 Macro Score: 0.93929 - 8s - loss: 0.1018 - acc: 0.9666 - val_loss: 0.0876 - val_acc: 0.9696 Epoch 14/180 F1 Macro Score: 0.93938 - 8s - loss: 0.1015 - acc: 0.9666 - val_loss: 0.0865 - val_acc: 0.9696 Epoch 15/180 F1 Macro Score: 0.93749 - 7s - loss: 0.1001 - acc: 0.9666 - val_loss: 0.0906 - val_acc: 0.9687 Epoch 16/180 F1 Macro Score: 0.93876 - 8s - loss: 0.1003 - acc: 0.9666 - val_loss: 0.0869 - val_acc: 0.9694 Epoch 17/180 F1 Macro Score: 0.93847 - 7s - loss: 0.0998 - acc: 0.9666 - val_loss: 0.0873 - val_acc: 0.9694 Epoch 18/180 F1 Macro Score: 0.93730 - 8s - loss: 0.0978 - acc: 0.9668 - val_loss: 0.0892 - val_acc: 0.9687 Epoch 19/180 F1 Macro Score: 0.93846 - 8s - loss: 0.0981 - acc: 0.9667 - val_loss: 0.0852 - val_acc: 0.9694 Epoch 20/180 F1 Macro Score: 0.93787 - 8s - loss: 0.0975 - acc: 0.9666 - val_loss: 0.0870 - val_acc: 0.9691 Epoch 21/180 F1 Macro Score: 0.93768 - 7s - loss: 0.0974 - acc: 0.9667 - val_loss: 0.0907 - val_acc: 0.9687 Epoch 22/180 F1 Macro Score: 0.93835 - 7s - loss: 0.1019 - acc: 0.9663 - val_loss: 0.0862 - val_acc: 0.9692 Epoch 23/180 F1 Macro Score: 0.93906 - 7s - loss: 0.0957 - acc: 0.9669 - val_loss: 0.0834 - val_acc: 0.9696 Epoch 24/180 F1 Macro Score: 0.93872 - 7s - loss: 0.0947 - acc: 0.9669 - val_loss: 0.0846 - val_acc: 0.9694 Epoch 25/180 F1 Macro Score: 0.93923 - 7s - loss: 0.0945 - acc: 0.9670 - val_loss: 0.0828 - val_acc: 0.9697 Epoch 26/180 F1 Macro Score: 0.93838 - 7s - loss: 0.0935 - acc: 0.9670 - val_loss: 0.0841 - val_acc: 0.9693 Epoch 27/180 F1 Macro Score: 0.93939 - 7s - loss: 0.0930 - acc: 0.9670 - val_loss: 0.0833 - val_acc: 0.9696 Epoch 28/180 F1 Macro Score: 0.93659 - 7s - loss: 0.0931 - acc: 0.9670 - val_loss: 0.0964 - val_acc: 0.9669 Epoch 29/180 F1 Macro Score: 0.93881 - 7s - loss: 0.0940 - acc: 0.9671 - val_loss: 0.0835 - val_acc: 0.9694 Epoch 30/180 F1 Macro Score: 0.93559 - 7s - loss: 0.0949 - acc: 0.9668 - val_loss: 0.0986 - val_acc: 0.9676 Epoch 31/180 F1 Macro Score: 0.93852 - 7s - loss: 0.0962 - acc: 0.9669 - val_loss: 0.0836 - val_acc: 0.9695 Epoch 32/180 F1 Macro Score: 0.93976 - 7s - loss: 0.0909 - acc: 0.9675 - val_loss: 0.0811 - val_acc: 0.9699 Epoch 33/180 F1 Macro Score: 0.93971 - 7s - loss: 0.0902 - acc: 0.9675 - val_loss: 0.0811 - val_acc: 0.9699 Epoch 34/180 F1 Macro Score: 0.93952 - 7s - loss: 0.0897 - acc: 0.9676 - val_loss: 0.0808 - val_acc: 0.9698 Epoch 35/180 F1 Macro Score: 0.93984 - 7s - loss: 0.0890 - acc: 0.9676 - val_loss: 0.0806 - val_acc: 0.9700 Epoch 36/180 F1 Macro Score: 0.93987 - 7s - loss: 0.0887 - acc: 0.9677 - val_loss: 0.0803 - val_acc: 0.9700 Epoch 37/180 F1 Macro Score: 0.93946 - 7s - loss: 0.0888 - acc: 0.9678 - val_loss: 0.0816 - val_acc: 0.9698 Epoch 38/180 F1 Macro Score: 0.93982 - 7s - loss: 0.0887 - acc: 0.9678 - val_loss: 0.0802 - val_acc: 0.9701 Epoch 39/180 F1 Macro Score: 0.93976 - 7s - loss: 0.0874 - acc: 0.9681 - val_loss: 0.0805 - val_acc: 0.9701 Epoch 40/180 F1 Macro Score: 0.94001 - 7s - loss: 0.0878 - acc: 0.9680 - val_loss: 0.0798 - val_acc: 0.9702 Epoch 41/180 F1 Macro Score: 0.94035 - 7s - loss: 0.0873 - acc: 0.9681 - val_loss: 0.0791 - val_acc: 0.9703 Epoch 42/180 F1 Macro Score: 0.94025 - 9s - loss: 0.0865 - acc: 0.9683 - val_loss: 0.0791 - val_acc: 0.9703 Epoch 43/180 F1 Macro Score: 0.94051 - 9s - loss: 0.0864 - acc: 0.9683 - val_loss: 0.0787 - val_acc: 0.9705 Epoch 44/180 F1 Macro Score: 0.94028 - 7s - loss: 0.0862 - acc: 0.9683 - val_loss: 0.0791 - val_acc: 0.9704 Epoch 45/180 F1 Macro Score: 0.94030 - 7s - loss: 0.0863 - acc: 0.9684 - val_loss: 0.0793 - val_acc: 0.9703 Epoch 46/180 F1 Macro Score: 0.94069 - 7s - loss: 0.0861 - acc: 0.9684 - val_loss: 0.0787 - val_acc: 0.9705 Epoch 47/180 F1 Macro Score: 0.94083 - 7s - loss: 0.0856 - acc: 0.9686 - val_loss: 0.0786 - val_acc: 0.9706 Epoch 48/180 F1 Macro Score: 0.94083 - 7s - loss: 0.0850 - acc: 0.9688 - val_loss: 0.0782 - val_acc: 0.9706 Epoch 49/180 F1 Macro Score: 0.94124 - 7s - loss: 0.0847 - acc: 0.9689 - val_loss: 0.0778 - val_acc: 0.9708 Epoch 50/180 F1 Macro Score: 0.94096 - 7s - loss: 0.0850 - acc: 0.9688 - val_loss: 0.0784 - val_acc: 0.9706 Epoch 51/180 F1 Macro Score: 0.94140 - 7s - loss: 0.0845 - acc: 0.9689 - val_loss: 0.0780 - val_acc: 0.9708 Epoch 52/180 F1 Macro Score: 0.94145 - 7s - loss: 0.0837 - acc: 0.9691 - val_loss: 0.0774 - val_acc: 0.9709 Epoch 53/180 F1 Macro Score: 0.94088 - 7s - loss: 0.0833 - acc: 0.9692 - val_loss: 0.0779 - val_acc: 0.9707 Epoch 54/180 F1 Macro Score: 0.94158 - 7s - loss: 0.0840 - acc: 0.9691 - val_loss: 0.0774 - val_acc: 0.9709 Epoch 55/180 F1 Macro Score: 0.94140 - 7s - loss: 0.0838 - acc: 0.9691 - val_loss: 0.0778 - val_acc: 0.9709 Epoch 56/180 F1 Macro Score: 0.94131 - 7s - loss: 0.0840 - acc: 0.9691 - val_loss: 0.0778 - val_acc: 0.9708 Epoch 57/180 F1 Macro Score: 0.94079 - 7s - loss: 0.0833 - acc: 0.9692 - val_loss: 0.0783 - val_acc: 0.9706 Epoch 58/180 F1 Macro Score: 0.94166 - 7s - loss: 0.0831 - acc: 0.9693 - val_loss: 0.0773 - val_acc: 0.9709 Epoch 59/180 F1 Macro Score: 0.94174 - 7s - loss: 0.0833 - acc: 0.9693 - val_loss: 0.0775 - val_acc: 0.9709 Epoch 60/180 F1 Macro Score: 0.94141 - 7s - loss: 0.0836 - acc: 0.9691 - val_loss: 0.0778 - val_acc: 0.9708 Epoch 61/180 F1 Macro Score: 0.94170 - 7s - loss: 0.0825 - acc: 0.9694 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 62/180 F1 Macro Score: 0.94180 - 8s - loss: 0.0820 - acc: 0.9695 - val_loss: 0.0770 - val_acc: 0.9710 Epoch 63/180 F1 Macro Score: 0.94169 - 7s - loss: 0.0822 - acc: 0.9695 - val_loss: 0.0772 - val_acc: 0.9711 Epoch 64/180 F1 Macro Score: 0.94187 - 7s - loss: 0.0825 - acc: 0.9693 - val_loss: 0.0769 - val_acc: 0.9711 Epoch 65/180 F1 Macro Score: 0.94166 - 7s - loss: 0.0823 - acc: 0.9696 - val_loss: 0.0770 - val_acc: 0.9710 Epoch 66/180 F1 Macro Score: 0.94191 - 7s - loss: 0.0823 - acc: 0.9695 - val_loss: 0.0772 - val_acc: 0.9710 Epoch 67/180 F1 Macro Score: 0.94134 - 7s - loss: 0.0820 - acc: 0.9696 - val_loss: 0.0774 - val_acc: 0.9708 Epoch 68/180 F1 Macro Score: 0.94161 - 7s - loss: 0.0825 - acc: 0.9694 - val_loss: 0.0774 - val_acc: 0.9708 Epoch 69/180 F1 Macro Score: 0.94170 - 7s - loss: 0.0819 - acc: 0.9696 - val_loss: 0.0772 - val_acc: 0.9710 Epoch 70/180 F1 Macro Score: 0.94167 - 7s - loss: 0.0819 - acc: 0.9697 - val_loss: 0.0772 - val_acc: 0.9709 Epoch 71/180 F1 Macro Score: 0.94097 - 7s - loss: 0.0816 - acc: 0.9697 - val_loss: 0.0779 - val_acc: 0.9707 Epoch 72/180 F1 Macro Score: 0.94158 - 7s - loss: 0.0816 - acc: 0.9697 - val_loss: 0.0771 - val_acc: 0.9709 Epoch 73/180 F1 Macro Score: 0.94166 - 7s - loss: 0.0811 - acc: 0.9698 - val_loss: 0.0770 - val_acc: 0.9710 Epoch 74/180 F1 Macro Score: 0.94166 - 7s - loss: 0.0815 - acc: 0.9697 - val_loss: 0.0774 - val_acc: 0.9709 Epoch 75/180 F1 Macro Score: 0.94164 - 7s - loss: 0.0813 - acc: 0.9697 - val_loss: 0.0771 - val_acc: 0.9710 Epoch 76/180 F1 Macro Score: 0.94179 - 7s - loss: 0.0809 - acc: 0.9698 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 77/180 F1 Macro Score: 0.94181 - 7s - loss: 0.0811 - acc: 0.9698 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 78/180 F1 Macro Score: 0.94150 - 7s - loss: 0.0811 - acc: 0.9698 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 79/180 F1 Macro Score: 0.94150 - 7s - loss: 0.0806 - acc: 0.9698 - val_loss: 0.0772 - val_acc: 0.9709 Epoch 80/180 F1 Macro Score: 0.94142 - 7s - loss: 0.0812 - acc: 0.9698 - val_loss: 0.0772 - val_acc: 0.9709 Epoch 81/180 F1 Macro Score: 0.94187 - 7s - loss: 0.0808 - acc: 0.9699 - val_loss: 0.0769 - val_acc: 0.9711 Epoch 82/180 F1 Macro Score: 0.94169 - 7s - loss: 0.0803 - acc: 0.9700 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 83/180 F1 Macro Score: 0.94187 - 7s - loss: 0.0801 - acc: 0.9701 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 84/180 F1 Macro Score: 0.94138 - 7s - loss: 0.0808 - acc: 0.9699 - val_loss: 0.0774 - val_acc: 0.9709 Epoch 85/180 F1 Macro Score: 0.94171 - 7s - loss: 0.0802 - acc: 0.9700 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 86/180 F1 Macro Score: 0.94175 - 7s - loss: 0.0809 - acc: 0.9698 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 87/180 F1 Macro Score: 0.94119 - 7s - loss: 0.0803 - acc: 0.9701 - val_loss: 0.0772 - val_acc: 0.9708 Epoch 88/180 F1 Macro Score: 0.94152 - 7s - loss: 0.0800 - acc: 0.9700 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 89/180 F1 Macro Score: 0.94163 - 7s - loss: 0.0802 - acc: 0.9700 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 90/180 F1 Macro Score: 0.94167 - 7s - loss: 0.0800 - acc: 0.9701 - val_loss: 0.0770 - val_acc: 0.9710 Epoch 91/180 F1 Macro Score: 0.94175 - 7s - loss: 0.0795 - acc: 0.9702 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 92/180 F1 Macro Score: 0.94182 - 7s - loss: 0.0794 - acc: 0.9702 - val_loss: 0.0767 - val_acc: 0.9710 Epoch 93/180 F1 Macro Score: 0.94178 - 7s - loss: 0.0794 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 94/180 F1 Macro Score: 0.94180 - 7s - loss: 0.0796 - acc: 0.9702 - val_loss: 0.0767 - val_acc: 0.9710 Epoch 95/180 F1 Macro Score: 0.94185 - 7s - loss: 0.0793 - acc: 0.9703 - val_loss: 0.0767 - val_acc: 0.9711 Epoch 96/180 F1 Macro Score: 0.94177 - 7s - loss: 0.0791 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 97/180 F1 Macro Score: 0.94181 - 7s - loss: 0.0790 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 98/180 F1 Macro Score: 0.94183 - 7s - loss: 0.0789 - acc: 0.9703 - val_loss: 0.0767 - val_acc: 0.9710 Epoch 99/180 F1 Macro Score: 0.94180 - 7s - loss: 0.0792 - acc: 0.9702 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 100/180 F1 Macro Score: 0.94181 - 7s - loss: 0.0791 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 101/180 F1 Macro Score: 0.94171 - 7s - loss: 0.0793 - acc: 0.9702 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 102/180 F1 Macro Score: 0.94176 - 7s - loss: 0.0792 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 103/180 F1 Macro Score: 0.94172 - 7s - loss: 0.0795 - acc: 0.9702 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 104/180 F1 Macro Score: 0.94171 - 8s - loss: 0.0790 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 105/180 F1 Macro Score: 0.94180 - 8s - loss: 0.0790 - acc: 0.9704 - val_loss: 0.0767 - val_acc: 0.9710 Epoch 106/180 F1 Macro Score: 0.94171 - 8s - loss: 0.0790 - acc: 0.9703 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 107/180 F1 Macro Score: 0.94171 - 7s - loss: 0.0790 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 108/180 F1 Macro Score: 0.94176 - 8s - loss: 0.0793 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 109/180 F1 Macro Score: 0.94189 - 8s - loss: 0.0789 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 110/180 F1 Macro Score: 0.94170 - 7s - loss: 0.0791 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 111/180 F1 Macro Score: 0.94173 - 7s - loss: 0.0787 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 112/180 F1 Macro Score: 0.94161 - 7s - loss: 0.0793 - acc: 0.9703 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 113/180 F1 Macro Score: 0.94168 - 7s - loss: 0.0795 - acc: 0.9702 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 114/180 F1 Macro Score: 0.94183 - 7s - loss: 0.0790 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 115/180 F1 Macro Score: 0.94168 - 7s - loss: 0.0794 - acc: 0.9703 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 116/180 F1 Macro Score: 0.94168 - 7s - loss: 0.0791 - acc: 0.9703 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 117/180 F1 Macro Score: 0.94169 - 7s - loss: 0.0793 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 118/180 F1 Macro Score: 0.94173 - 7s - loss: 0.0790 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 119/180 F1 Macro Score: 0.94177 - 7s - loss: 0.0791 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 120/180 F1 Macro Score: 0.94171 - 7s - loss: 0.0786 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 121/180 F1 Macro Score: 0.94179 - 7s - loss: 0.0787 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 122/180 F1 Macro Score: 0.94175 - 7s - loss: 0.0788 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 123/180 F1 Macro Score: 0.94166 - 7s - loss: 0.0793 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 124/180 F1 Macro Score: 0.94172 - 7s - loss: 0.0790 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 125/180 F1 Macro Score: 0.94174 - 7s - loss: 0.0790 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 126/180 F1 Macro Score: 0.94166 - 7s - loss: 0.0792 - acc: 0.9703 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 127/180 F1 Macro Score: 0.94177 - 7s - loss: 0.0784 - acc: 0.9705 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 128/180 F1 Macro Score: 0.94171 - 9s - loss: 0.0786 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 129/180 F1 Macro Score: 0.94169 - 9s - loss: 0.0788 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 130/180 F1 Macro Score: 0.94170 - 8s - loss: 0.0789 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 131/180 F1 Macro Score: 0.94170 - 8s - loss: 0.0789 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 132/180 F1 Macro Score: 0.94166 - 8s - loss: 0.0793 - acc: 0.9703 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 133/180 F1 Macro Score: 0.94165 - 8s - loss: 0.0784 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 134/180 F1 Macro Score: 0.94177 - 8s - loss: 0.0789 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 135/180 F1 Macro Score: 0.94173 - 7s - loss: 0.0788 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 136/180 F1 Macro Score: 0.94160 - 9s - loss: 0.0786 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 137/180 F1 Macro Score: 0.94162 - 8s - loss: 0.0787 - acc: 0.9704 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 138/180 F1 Macro Score: 0.94163 - 7s - loss: 0.0787 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 139/180 F1 Macro Score: 0.94167 - 8s - loss: 0.0789 - acc: 0.9704 - val_loss: 0.0768 - val_acc: 0.9710 Epoch 140/180 F1 Macro Score: 0.94161 - 7s - loss: 0.0789 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 141/180 F1 Macro Score: 0.94175 - 7s - loss: 0.0788 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 142/180 F1 Macro Score: 0.94166 - 7s - loss: 0.0785 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 143/180 F1 Macro Score: 0.94169 - 7s - loss: 0.0786 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 144/180 F1 Macro Score: 0.94169 - 8s - loss: 0.0784 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 145/180 F1 Macro Score: 0.94167 - 7s - loss: 0.0786 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 146/180 F1 Macro Score: 0.94166 - 7s - loss: 0.0785 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 147/180 F1 Macro Score: 0.94161 - 7s - loss: 0.0785 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 148/180 F1 Macro Score: 0.94165 - 7s - loss: 0.0788 - acc: 0.9704 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 149/180 F1 Macro Score: 0.94152 - 7s - loss: 0.0786 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9708 Epoch 150/180 F1 Macro Score: 0.94164 - 7s - loss: 0.0787 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 151/180 F1 Macro Score: 0.94166 - 7s - loss: 0.0786 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 152/180 F1 Macro Score: 0.94158 - 7s - loss: 0.0787 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 153/180 F1 Macro Score: 0.94166 - 7s - loss: 0.0789 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 154/180 F1 Macro Score: 0.94159 - 7s - loss: 0.0786 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 155/180 F1 Macro Score: 0.94160 - 7s - loss: 0.0784 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 156/180 F1 Macro Score: 0.94167 - 7s - loss: 0.0785 - acc: 0.9704 - val_loss: 0.0769 - val_acc: 0.9710 Epoch 157/180 F1 Macro Score: 0.94163 - 7s - loss: 0.0784 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 158/180 F1 Macro Score: 0.94161 - 7s - loss: 0.0786 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 159/180 F1 Macro Score: 0.94146 - 7s - loss: 0.0787 - acc: 0.9704 - val_loss: 0.0770 - val_acc: 0.9708 Epoch 160/180 F1 Macro Score: 0.94157 - 7s - loss: 0.0785 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 161/180 F1 Macro Score: 0.94163 - 7s - loss: 0.0784 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 162/180 F1 Macro Score: 0.94159 - 7s - loss: 0.0783 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 163/180 F1 Macro Score: 0.94155 - 7s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 164/180 F1 Macro Score: 0.94158 - 7s - loss: 0.0782 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 165/180 F1 Macro Score: 0.94160 - 7s - loss: 0.0786 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 166/180 F1 Macro Score: 0.94159 - 7s - loss: 0.0785 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 167/180 F1 Macro Score: 0.94158 - 7s - loss: 0.0786 - acc: 0.9705 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 168/180 F1 Macro Score: 0.94162 - 7s - loss: 0.0788 - acc: 0.9704 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 169/180 F1 Macro Score: 0.94161 - 7s - loss: 0.0781 - acc: 0.9706 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 170/180 F1 Macro Score: 0.94164 - 7s - loss: 0.0785 - acc: 0.9705 - val_loss: 0.0771 - val_acc: 0.9709 Epoch 171/180 F1 Macro Score: 0.94158 - 8s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 172/180 F1 Macro Score: 0.94162 - 8s - loss: 0.0783 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 173/180 F1 Macro Score: 0.94156 - 8s - loss: 0.0784 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 174/180 F1 Macro Score: 0.94160 - 8s - loss: 0.0785 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 175/180 F1 Macro Score: 0.94163 - 7s - loss: 0.0784 - acc: 0.9704 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 176/180 F1 Macro Score: 0.94152 - 7s - loss: 0.0784 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 177/180 F1 Macro Score: 0.94156 - 7s - loss: 0.0784 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 178/180 F1 Macro Score: 0.94158 - 7s - loss: 0.0785 - acc: 0.9706 - val_loss: 0.0769 - val_acc: 0.9709 Epoch 179/180 F1 Macro Score: 0.94162 - 7s - loss: 0.0783 - acc: 0.9705 - val_loss: 0.0770 - val_acc: 0.9709 Epoch 180/180 F1 Macro Score: 0.94169 - 7s - loss: 0.0781 - acc: 0.9706 - val_loss: 0.0770 - val_acc: 0.9709 Training fold 2 completed. macro f1 score : 0.94169 Our training dataset shape is (1000, 4000, 19) Our validation dataset shape is (250, 4000, 19) Train on 1000 samples, validate on 250 samples Epoch 1/180 F1 Macro Score: 0.68362 - 19s - loss: 0.4988 - acc: 0.8627 - val_loss: 1.0696 - val_acc: 0.7235 Epoch 2/180 F1 Macro Score: 0.91739 - 7s - loss: 0.1653 - acc: 0.9602 - val_loss: 0.3926 - val_acc: 0.9601 Epoch 3/180 F1 Macro Score: 0.93307 - 7s - loss: 0.1411 - acc: 0.9640 - val_loss: 0.2317 - val_acc: 0.9666 Epoch 4/180 F1 Macro Score: 0.93435 - 7s - loss: 0.1262 - acc: 0.9655 - val_loss: 0.1320 - val_acc: 0.9676 Epoch 5/180 F1 Macro Score: 0.93570 - 7s - loss: 0.1165 - acc: 0.9663 - val_loss: 0.1101 - val_acc: 0.9680 Epoch 6/180 F1 Macro Score: 0.93662 - 7s - loss: 0.1154 - acc: 0.9662 - val_loss: 0.0992 - val_acc: 0.9683 Epoch 7/180 F1 Macro Score: 0.93510 - 7s - loss: 0.1142 - acc: 0.9662 - val_loss: 0.0994 - val_acc: 0.9679 Epoch 8/180 F1 Macro Score: 0.93542 - 7s - loss: 0.1096 - acc: 0.9665 - val_loss: 0.1091 - val_acc: 0.9676 Epoch 9/180 F1 Macro Score: 0.93501 - 7s - loss: 0.1089 - acc: 0.9665 - val_loss: 0.0947 - val_acc: 0.9681 Epoch 10/180 F1 Macro Score: 0.93387 - 7s - loss: 0.1052 - acc: 0.9667 - val_loss: 0.0966 - val_acc: 0.9672 Epoch 11/180 F1 Macro Score: 0.93696 - 7s - loss: 0.1033 - acc: 0.9668 - val_loss: 0.0908 - val_acc: 0.9685 Epoch 12/180 F1 Macro Score: 0.93637 - 7s - loss: 0.1024 - acc: 0.9667 - val_loss: 0.0908 - val_acc: 0.9682 Epoch 13/180 F1 Macro Score: 0.93592 - 7s - loss: 0.1011 - acc: 0.9669 - val_loss: 0.0912 - val_acc: 0.9684 Epoch 14/180 F1 Macro Score: 0.93683 - 7s - loss: 0.0993 - acc: 0.9670 - val_loss: 0.0896 - val_acc: 0.9685 Epoch 15/180 F1 Macro Score: 0.93707 - 7s - loss: 0.0987 - acc: 0.9670 - val_loss: 0.0939 - val_acc: 0.9685 Epoch 16/180 F1 Macro Score: 0.93455 - 7s - loss: 0.0992 - acc: 0.9668 - val_loss: 0.0920 - val_acc: 0.9675 Epoch 17/180 F1 Macro Score: 0.93480 - 7s - loss: 0.0985 - acc: 0.9669 - val_loss: 0.0922 - val_acc: 0.9676 Epoch 18/180 F1 Macro Score: 0.93643 - 7s - loss: 0.0975 - acc: 0.9670 - val_loss: 0.0890 - val_acc: 0.9684 Epoch 19/180 F1 Macro Score: 0.93662 - 7s - loss: 0.0961 - acc: 0.9671 - val_loss: 0.0873 - val_acc: 0.9684 Epoch 20/180 F1 Macro Score: 0.93596 - 7s - loss: 0.0971 - acc: 0.9669 - val_loss: 0.0993 - val_acc: 0.9677 Epoch 21/180 F1 Macro Score: 0.42415 - 7s - loss: 0.2905 - acc: 0.9161 - val_loss: 2.4618 - val_acc: 0.6838 Epoch 22/180 F1 Macro Score: 0.91542 - 8s - loss: 0.2210 - acc: 0.9357 - val_loss: 0.1428 - val_acc: 0.9598 Epoch 23/180 F1 Macro Score: 0.93362 - 7s - loss: 0.1246 - acc: 0.9645 - val_loss: 0.1097 - val_acc: 0.9669 Epoch 24/180 F1 Macro Score: 0.93502 - 7s - loss: 0.1173 - acc: 0.9655 - val_loss: 0.1036 - val_acc: 0.9676 Epoch 25/180 F1 Macro Score: 0.93390 - 7s - loss: 0.1116 - acc: 0.9663 - val_loss: 0.1011 - val_acc: 0.9673 Epoch 26/180 F1 Macro Score: 0.93641 - 7s - loss: 0.1095 - acc: 0.9665 - val_loss: 0.0963 - val_acc: 0.9682 Epoch 27/180 F1 Macro Score: 0.93631 - 7s - loss: 0.1065 - acc: 0.9667 - val_loss: 0.0946 - val_acc: 0.9682 Epoch 28/180 F1 Macro Score: 0.93620 - 7s - loss: 0.1051 - acc: 0.9668 - val_loss: 0.0935 - val_acc: 0.9682 Epoch 29/180 F1 Macro Score: 0.93600 - 7s - loss: 0.1042 - acc: 0.9669 - val_loss: 0.0933 - val_acc: 0.9681 Epoch 30/180 F1 Macro Score: 0.93710 - 7s - loss: 0.1038 - acc: 0.9667 - val_loss: 0.0913 - val_acc: 0.9686 Epoch 31/180 F1 Macro Score: 0.93667 - 7s - loss: 0.1009 - acc: 0.9671 - val_loss: 0.0907 - val_acc: 0.9685 Epoch 32/180 F1 Macro Score: 0.93743 - 7s - loss: 0.1005 - acc: 0.9672 - val_loss: 0.0901 - val_acc: 0.9687 Epoch 33/180 F1 Macro Score: 0.93738 - 7s - loss: 0.0996 - acc: 0.9673 - val_loss: 0.0898 - val_acc: 0.9687 Epoch 34/180 F1 Macro Score: 0.93719 - 7s - loss: 0.1005 - acc: 0.9671 - val_loss: 0.0896 - val_acc: 0.9686 Epoch 35/180 F1 Macro Score: 0.93710 - 7s - loss: 0.0994 - acc: 0.9672 - val_loss: 0.0895 - val_acc: 0.9686 Epoch 36/180 F1 Macro Score: 0.93709 - 7s - loss: 0.0991 - acc: 0.9672 - val_loss: 0.0891 - val_acc: 0.9686 Epoch 37/180 F1 Macro Score: 0.93670 - 7s - loss: 0.0992 - acc: 0.9672 - val_loss: 0.0898 - val_acc: 0.9686 Epoch 38/180 F1 Macro Score: 0.93699 - 7s - loss: 0.0989 - acc: 0.9673 - val_loss: 0.0892 - val_acc: 0.9686 Epoch 39/180 F1 Macro Score: 0.93691 - 7s - loss: 0.0984 - acc: 0.9672 - val_loss: 0.0891 - val_acc: 0.9686 Epoch 40/180 F1 Macro Score: 0.93704 - 7s - loss: 0.0986 - acc: 0.9671 - val_loss: 0.0891 - val_acc: 0.9686 Epoch 41/180 F1 Macro Score: 0.93724 - 7s - loss: 0.0977 - acc: 0.9672 - val_loss: 0.0886 - val_acc: 0.9687 Epoch 42/180 F1 Macro Score: 0.93722 - 7s - loss: 0.0969 - acc: 0.9673 - val_loss: 0.0882 - val_acc: 0.9688 Epoch 43/180 F1 Macro Score: 0.93728 - 7s - loss: 0.0971 - acc: 0.9673 - val_loss: 0.0881 - val_acc: 0.9687 Epoch 44/180 F1 Macro Score: 0.93716 - 7s - loss: 0.0970 - acc: 0.9673 - val_loss: 0.0878 - val_acc: 0.9687 Epoch 45/180 F1 Macro Score: 0.93734 - 7s - loss: 0.0970 - acc: 0.9673 - val_loss: 0.0878 - val_acc: 0.9688 Epoch 46/180 F1 Macro Score: 0.93739 - 8s - loss: 0.0966 - acc: 0.9674 - val_loss: 0.0877 - val_acc: 0.9688 Epoch 47/180 F1 Macro Score: 0.93726 - 7s - loss: 0.0977 - acc: 0.9672 - val_loss: 0.0877 - val_acc: 0.9688 Epoch 48/180 F1 Macro Score: 0.93742 - 7s - loss: 0.0967 - acc: 0.9673 - val_loss: 0.0874 - val_acc: 0.9688 Epoch 49/180 F1 Macro Score: 0.93731 - 7s - loss: 0.0965 - acc: 0.9673 - val_loss: 0.0874 - val_acc: 0.9688 Epoch 50/180 F1 Macro Score: 0.93714 - 7s - loss: 0.0959 - acc: 0.9673 - val_loss: 0.0873 - val_acc: 0.9688 Epoch 51/180 F1 Macro Score: 0.93720 - 7s - loss: 0.0963 - acc: 0.9673 - val_loss: 0.0872 - val_acc: 0.9687 Epoch 52/180 F1 Macro Score: 0.93701 - 7s - loss: 0.0964 - acc: 0.9673 - val_loss: 0.0874 - val_acc: 0.9687 Epoch 53/180 F1 Macro Score: 0.93734 - 7s - loss: 0.0954 - acc: 0.9674 - val_loss: 0.0873 - val_acc: 0.9688 Epoch 54/180 F1 Macro Score: 0.93717 - 7s - loss: 0.0964 - acc: 0.9672 - val_loss: 0.0871 - val_acc: 0.9687 Epoch 55/180 F1 Macro Score: 0.93750 - 7s - loss: 0.0960 - acc: 0.9673 - val_loss: 0.0867 - val_acc: 0.9688 Epoch 56/180 F1 Macro Score: 0.93735 - 7s - loss: 0.0957 - acc: 0.9673 - val_loss: 0.0869 - val_acc: 0.9688 Epoch 57/180 F1 Macro Score: 0.93752 - 7s - loss: 0.0950 - acc: 0.9674 - val_loss: 0.0867 - val_acc: 0.9688 Epoch 58/180 F1 Macro Score: 0.93722 - 7s - loss: 0.0949 - acc: 0.9674 - val_loss: 0.0866 - val_acc: 0.9688 Epoch 59/180 F1 Macro Score: 0.93724 - 7s - loss: 0.0952 - acc: 0.9674 - val_loss: 0.0867 - val_acc: 0.9688 Epoch 60/180 F1 Macro Score: 0.93733 - 7s - loss: 0.0947 - acc: 0.9674 - val_loss: 0.0863 - val_acc: 0.9688 Epoch 61/180 F1 Macro Score: 0.93726 - 7s - loss: 0.0944 - acc: 0.9674 - val_loss: 0.0862 - val_acc: 0.9688 Epoch 62/180 F1 Macro Score: 0.93737 - 7s - loss: 0.0947 - acc: 0.9674 - val_loss: 0.0863 - val_acc: 0.9689 Epoch 63/180 F1 Macro Score: 0.93726 - 7s - loss: 0.0945 - acc: 0.9674 - val_loss: 0.0861 - val_acc: 0.9688 Epoch 64/180 F1 Macro Score: 0.93741 - 7s - loss: 0.0943 - acc: 0.9674 - val_loss: 0.0860 - val_acc: 0.9689 Epoch 65/180 F1 Macro Score: 0.93725 - 7s - loss: 0.0951 - acc: 0.9673 - val_loss: 0.0864 - val_acc: 0.9688 Epoch 66/180 F1 Macro Score: 0.93734 - 7s - loss: 0.0941 - acc: 0.9674 - val_loss: 0.0860 - val_acc: 0.9688 Epoch 67/180 F1 Macro Score: 0.93730 - 7s - loss: 0.0940 - acc: 0.9674 - val_loss: 0.0859 - val_acc: 0.9689 Epoch 68/180 F1 Macro Score: 0.93639 - 7s - loss: 0.0943 - acc: 0.9674 - val_loss: 0.0879 - val_acc: 0.9683 Epoch 69/180 F1 Macro Score: 0.93723 - 7s - loss: 0.0940 - acc: 0.9674 - val_loss: 0.0862 - val_acc: 0.9688 Epoch 70/180 F1 Macro Score: 0.93743 - 7s - loss: 0.0944 - acc: 0.9674 - val_loss: 0.0858 - val_acc: 0.9688 Epoch 71/180 F1 Macro Score: 0.93736 - 7s - loss: 0.0946 - acc: 0.9673 - val_loss: 0.0857 - val_acc: 0.9688 Epoch 72/180 F1 Macro Score: 0.93748 - 7s - loss: 0.0944 - acc: 0.9674 - val_loss: 0.0858 - val_acc: 0.9689 Epoch 73/180 F1 Macro Score: 0.93779 - 7s - loss: 0.0936 - acc: 0.9674 - val_loss: 0.0855 - val_acc: 0.9689 Epoch 74/180 F1 Macro Score: 0.93746 - 7s - loss: 0.0936 - acc: 0.9675 - val_loss: 0.0858 - val_acc: 0.9689 Epoch 75/180 F1 Macro Score: 0.93739 - 7s - loss: 0.0933 - acc: 0.9675 - val_loss: 0.0855 - val_acc: 0.9689 Epoch 76/180 F1 Macro Score: 0.93745 - 7s - loss: 0.0926 - acc: 0.9675 - val_loss: 0.0853 - val_acc: 0.9689 Epoch 77/180 F1 Macro Score: 0.93732 - 7s - loss: 0.0932 - acc: 0.9675 - val_loss: 0.0853 - val_acc: 0.9688 Epoch 78/180 F1 Macro Score: 0.93744 - 7s - loss: 0.0930 - acc: 0.9675 - val_loss: 0.0852 - val_acc: 0.9689 Epoch 79/180 F1 Macro Score: 0.93728 - 7s - loss: 0.0929 - acc: 0.9675 - val_loss: 0.0856 - val_acc: 0.9688 Epoch 80/180 F1 Macro Score: 0.93749 - 7s - loss: 0.0923 - acc: 0.9676 - val_loss: 0.0850 - val_acc: 0.9689 Epoch 81/180 F1 Macro Score: 0.93745 - 7s - loss: 0.0927 - acc: 0.9675 - val_loss: 0.0853 - val_acc: 0.9689 Epoch 82/180 F1 Macro Score: 0.93753 - 7s - loss: 0.0931 - acc: 0.9674 - val_loss: 0.0850 - val_acc: 0.9689 Epoch 83/180 F1 Macro Score: 0.93745 - 7s - loss: 0.0927 - acc: 0.9675 - val_loss: 0.0851 - val_acc: 0.9689 Epoch 84/180 F1 Macro Score: 0.93755 - 7s - loss: 0.0922 - acc: 0.9675 - val_loss: 0.0849 - val_acc: 0.9689 Epoch 85/180 F1 Macro Score: 0.93755 - 7s - loss: 0.0922 - acc: 0.9676 - val_loss: 0.0849 - val_acc: 0.9690 Epoch 86/180 F1 Macro Score: 0.93743 - 7s - loss: 0.0935 - acc: 0.9674 - val_loss: 0.0849 - val_acc: 0.9689 Epoch 87/180 F1 Macro Score: 0.93760 - 7s - loss: 0.0938 - acc: 0.9674 - val_loss: 0.0851 - val_acc: 0.9690 Epoch 88/180 F1 Macro Score: 0.93718 - 7s - loss: 0.0936 - acc: 0.9674 - val_loss: 0.0855 - val_acc: 0.9689 Epoch 89/180 F1 Macro Score: 0.93699 - 7s - loss: 0.0925 - acc: 0.9675 - val_loss: 0.0850 - val_acc: 0.9689 Epoch 90/180 F1 Macro Score: 0.93773 - 7s - loss: 0.0922 - acc: 0.9675 - val_loss: 0.0849 - val_acc: 0.9690 Epoch 91/180 F1 Macro Score: 0.93768 - 7s - loss: 0.0922 - acc: 0.9676 - val_loss: 0.0846 - val_acc: 0.9690 Epoch 92/180 F1 Macro Score: 0.93767 - 7s - loss: 0.0923 - acc: 0.9675 - val_loss: 0.0846 - val_acc: 0.9690 Epoch 93/180 F1 Macro Score: 0.93759 - 7s - loss: 0.0916 - acc: 0.9676 - val_loss: 0.0846 - val_acc: 0.9690 Epoch 94/180 F1 Macro Score: 0.93766 - 7s - loss: 0.0914 - acc: 0.9677 - val_loss: 0.0846 - val_acc: 0.9690 Epoch 95/180 F1 Macro Score: 0.93753 - 7s - loss: 0.0914 - acc: 0.9677 - val_loss: 0.0846 - val_acc: 0.9689 Epoch 96/180 F1 Macro Score: 0.93751 - 7s - loss: 0.0917 - acc: 0.9676 - val_loss: 0.0847 - val_acc: 0.9690 Epoch 97/180 F1 Macro Score: 0.93771 - 7s - loss: 0.0916 - acc: 0.9677 - val_loss: 0.0845 - val_acc: 0.9690 Epoch 98/180 F1 Macro Score: 0.93770 - 7s - loss: 0.0913 - acc: 0.9677 - val_loss: 0.0845 - val_acc: 0.9690 Epoch 99/180 F1 Macro Score: 0.93768 - 7s - loss: 0.0918 - acc: 0.9676 - val_loss: 0.0845 - val_acc: 0.9690 Epoch 100/180 F1 Macro Score: 0.93767 - 7s - loss: 0.0919 - acc: 0.9676 - val_loss: 0.0845 - val_acc: 0.9690 Epoch 101/180 F1 Macro Score: 0.93769 - 7s - loss: 0.0917 - acc: 0.9677 - val_loss: 0.0845 - val_acc: 0.9690 Epoch 102/180 F1 Macro Score: 0.93765 - 7s - loss: 0.0920 - acc: 0.9675 - val_loss: 0.0845 - val_acc: 0.9690 Epoch 103/180 F1 Macro Score: 0.93774 - 7s - loss: 0.0918 - acc: 0.9675 - val_loss: 0.0845 - val_acc: 0.9690 Epoch 104/180 F1 Macro Score: 0.93772 - 7s - loss: 0.0913 - acc: 0.9677 - val_loss: 0.0845 - val_acc: 0.9690 Epoch 105/180 F1 Macro Score: 0.93768 - 7s - loss: 0.0914 - acc: 0.9677 - val_loss: 0.0845 - val_acc: 0.9690 Epoch 106/180 F1 Macro Score: 0.93769 - 7s - loss: 0.0910 - acc: 0.9677 - val_loss: 0.0845 - val_acc: 0.9690 Epoch 107/180 F1 Macro Score: 0.93770 - 7s - loss: 0.0913 - acc: 0.9676 - val_loss: 0.0844 - val_acc: 0.9690 Epoch 108/180 F1 Macro Score: 0.93774 - 7s - loss: 0.0915 - acc: 0.9676 - val_loss: 0.0845 - val_acc: 0.9690 Epoch 109/180 F1 Macro Score: 0.93769 - 7s - loss: 0.0915 - acc: 0.9676 - val_loss: 0.0844 - val_acc: 0.9690 Epoch 110/180 F1 Macro Score: 0.93774 - 7s - loss: 0.0913 - acc: 0.9676 - val_loss: 0.0844 - val_acc: 0.9690 Epoch 111/180 F1 Macro Score: 0.93772 - 7s - loss: 0.0913 - acc: 0.9677 - val_loss: 0.0843 - val_acc: 0.9690 Epoch 112/180 F1 Macro Score: 0.93774 - 8s - loss: 0.0912 - acc: 0.9677 - val_loss: 0.0844 - val_acc: 0.9690 Epoch 113/180 F1 Macro Score: 0.93770 - 8s - loss: 0.0911 - acc: 0.9677 - val_loss: 0.0844 - val_acc: 0.9690 Epoch 114/180 F1 Macro Score: 0.93774 - 8s - loss: 0.0913 - acc: 0.9677 - val_loss: 0.0843 - val_acc: 0.9690 Epoch 115/180 F1 Macro Score: 0.93779 - 7s - loss: 0.0920 - acc: 0.9676 - val_loss: 0.0843 - val_acc: 0.9690 Epoch 116/180 F1 Macro Score: 0.93775 - 8s - loss: 0.0912 - acc: 0.9677 - val_loss: 0.0843 - val_acc: 0.9690 Epoch 117/180 F1 Macro Score: 0.93776 - 7s - loss: 0.0906 - acc: 0.9677 - val_loss: 0.0843 - val_acc: 0.9690 Epoch 118/180 F1 Macro Score: 0.93736 - 7s - loss: 0.0918 - acc: 0.9676 - val_loss: 0.0845 - val_acc: 0.9689 Epoch 119/180 F1 Macro Score: 0.93775 - 7s - loss: 0.0914 - acc: 0.9676 - val_loss: 0.0843 - val_acc: 0.9690 Epoch 120/180 F1 Macro Score: 0.93764 - 8s - loss: 0.0911 - acc: 0.9676 - val_loss: 0.0843 - val_acc: 0.9690 Epoch 121/180 F1 Macro Score: 0.93774 - 7s - loss: 0.0917 - acc: 0.9675 - val_loss: 0.0843 - val_acc: 0.9690 Epoch 122/180 F1 Macro Score: 0.93776 - 7s - loss: 0.0914 - acc: 0.9677 - val_loss: 0.0843 - val_acc: 0.9690 Epoch 123/180 F1 Macro Score: 0.93766 - 7s - loss: 0.0918 - acc: 0.9676 - val_loss: 0.0843 - val_acc: 0.9690 Epoch 124/180 F1 Macro Score: 0.93774 - 8s - loss: 0.0911 - acc: 0.9677 - val_loss: 0.0842 - val_acc: 0.9690 Epoch 125/180 F1 Macro Score: 0.93777 - 7s - loss: 0.0913 - acc: 0.9677 - val_loss: 0.0842 - val_acc: 0.9690 Epoch 126/180 F1 Macro Score: 0.93767 - 8s - loss: 0.0908 - acc: 0.9677 - val_loss: 0.0842 - val_acc: 0.9690 Epoch 127/180 F1 Macro Score: 0.93771 - 7s - loss: 0.0909 - acc: 0.9677 - val_loss: 0.0842 - val_acc: 0.9690 Epoch 128/180 F1 Macro Score: 0.93775 - 8s - loss: 0.0910 - acc: 0.9676 - val_loss: 0.0842 - val_acc: 0.9690 Epoch 129/180 F1 Macro Score: 0.93777 - 9s - loss: 0.0913 - acc: 0.9676 - val_loss: 0.0842 - val_acc: 0.9690 Epoch 130/180 F1 Macro Score: 0.93776 - 7s - loss: 0.0906 - acc: 0.9677 - val_loss: 0.0842 - val_acc: 0.9690 Epoch 131/180 F1 Macro Score: 0.93772 - 7s - loss: 0.0913 - acc: 0.9677 - val_loss: 0.0842 - val_acc: 0.9690 Epoch 132/180 F1 Macro Score: 0.93783 - 7s - loss: 0.0921 - acc: 0.9675 - val_loss: 0.0842 - val_acc: 0.9690 Epoch 133/180 F1 Macro Score: 0.93766 - 7s - loss: 0.0908 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 134/180 F1 Macro Score: 0.93775 - 7s - loss: 0.0911 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 135/180 F1 Macro Score: 0.93748 - 7s - loss: 0.0908 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 136/180 F1 Macro Score: 0.93770 - 7s - loss: 0.0910 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 137/180 F1 Macro Score: 0.93767 - 7s - loss: 0.0910 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 138/180 F1 Macro Score: 0.93767 - 7s - loss: 0.0908 - acc: 0.9676 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 139/180 F1 Macro Score: 0.93774 - 7s - loss: 0.0905 - acc: 0.9678 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 140/180 F1 Macro Score: 0.93779 - 7s - loss: 0.0909 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 141/180 F1 Macro Score: 0.93781 - 7s - loss: 0.0911 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 142/180 F1 Macro Score: 0.93770 - 7s - loss: 0.0905 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 143/180 F1 Macro Score: 0.93770 - 7s - loss: 0.0909 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 144/180 F1 Macro Score: 0.93782 - 7s - loss: 0.0905 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 145/180 F1 Macro Score: 0.93773 - 7s - loss: 0.0907 - acc: 0.9676 - val_loss: 0.0840 - val_acc: 0.9690 Epoch 146/180 F1 Macro Score: 0.93772 - 7s - loss: 0.0906 - acc: 0.9677 - val_loss: 0.0840 - val_acc: 0.9690 Epoch 147/180 F1 Macro Score: 0.93776 - 7s - loss: 0.0908 - acc: 0.9677 - val_loss: 0.0840 - val_acc: 0.9690 Epoch 148/180 F1 Macro Score: 0.93775 - 7s - loss: 0.0912 - acc: 0.9676 - val_loss: 0.0840 - val_acc: 0.9690 Epoch 149/180 F1 Macro Score: 0.93776 - 7s - loss: 0.0913 - acc: 0.9676 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 150/180 F1 Macro Score: 0.93764 - 7s - loss: 0.0907 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 151/180 F1 Macro Score: 0.93767 - 7s - loss: 0.0909 - acc: 0.9676 - val_loss: 0.0840 - val_acc: 0.9690 Epoch 152/180 F1 Macro Score: 0.93759 - 7s - loss: 0.0907 - acc: 0.9677 - val_loss: 0.0841 - val_acc: 0.9690 Epoch 153/180 F1 Macro Score: 0.93778 - 7s - loss: 0.0904 - acc: 0.9677 - val_loss: 0.0839 - val_acc: 0.9690 Epoch 154/180 F1 Macro Score: 0.93784 - 7s - loss: 0.0907 - acc: 0.9677 - val_loss: 0.0839 - val_acc: 0.9690 Epoch 155/180 F1 Macro Score: 0.93781 - 7s - loss: 0.0903 - acc: 0.9677 - val_loss: 0.0839 - val_acc: 0.9690 Epoch 156/180 F1 Macro Score: 0.93776 - 7s - loss: 0.0903 - acc: 0.9677 - val_loss: 0.0839 - val_acc: 0.9690 Epoch 157/180 F1 Macro Score: 0.93781 - 7s - loss: 0.0901 - acc: 0.9678 - val_loss: 0.0839 - val_acc: 0.9690 Epoch 158/180 F1 Macro Score: 0.93767 - 7s - loss: 0.0903 - acc: 0.9678 - val_loss: 0.0839 - val_acc: 0.9690 Epoch 159/180 F1 Macro Score: 0.93777 - 7s - loss: 0.0916 - acc: 0.9675 - val_loss: 0.0839 - val_acc: 0.9690 Epoch 160/180 F1 Macro Score: 0.93773 - 7s - loss: 0.0905 - acc: 0.9677 - val_loss: 0.0839 - val_acc: 0.9690 Epoch 161/180 F1 Macro Score: 0.93778 - 7s - loss: 0.0904 - acc: 0.9677 - val_loss: 0.0839 - val_acc: 0.9690 Epoch 162/180 F1 Macro Score: 0.93769 - 8s - loss: 0.0907 - acc: 0.9677 - val_loss: 0.0839 - val_acc: 0.9690 Epoch 163/180 F1 Macro Score: 0.93774 - 8s - loss: 0.0903 - acc: 0.9677 - val_loss: 0.0839 - val_acc: 0.9690 Epoch 164/180 F1 Macro Score: 0.93771 - 8s - loss: 0.0907 - acc: 0.9676 - val_loss: 0.0838 - val_acc: 0.9690 Epoch 165/180 F1 Macro Score: 0.93760 - 8s - loss: 0.0906 - acc: 0.9677 - val_loss: 0.0838 - val_acc: 0.9690 Epoch 166/180 F1 Macro Score: 0.93779 - 8s - loss: 0.0904 - acc: 0.9677 - val_loss: 0.0838 - val_acc: 0.9690 Epoch 167/180 F1 Macro Score: 0.93780 - 8s - loss: 0.0900 - acc: 0.9677 - val_loss: 0.0838 - val_acc: 0.9690 Epoch 168/180 F1 Macro Score: 0.93780 - 8s - loss: 0.0907 - acc: 0.9677 - val_loss: 0.0838 - val_acc: 0.9690 Epoch 169/180 F1 Macro Score: 0.93775 - 8s - loss: 0.0905 - acc: 0.9677 - val_loss: 0.0838 - val_acc: 0.9690 Epoch 170/180 F1 Macro Score: 0.93773 - 8s - loss: 0.0902 - acc: 0.9677 - val_loss: 0.0838 - val_acc: 0.9690 Epoch 171/180 F1 Macro Score: 0.93784 - 8s - loss: 0.0901 - acc: 0.9677 - val_loss: 0.0837 - val_acc: 0.9691 Epoch 172/180 F1 Macro Score: 0.93789 - 7s - loss: 0.0905 - acc: 0.9678 - val_loss: 0.0837 - val_acc: 0.9690 Epoch 173/180 F1 Macro Score: 0.93768 - 8s - loss: 0.0907 - acc: 0.9676 - val_loss: 0.0838 - val_acc: 0.9690 Epoch 174/180 F1 Macro Score: 0.93788 - 8s - loss: 0.0910 - acc: 0.9676 - val_loss: 0.0837 - val_acc: 0.9691 Epoch 175/180 F1 Macro Score: 0.93762 - 8s - loss: 0.0905 - acc: 0.9677 - val_loss: 0.0838 - val_acc: 0.9690 Epoch 176/180 F1 Macro Score: 0.93776 - 8s - loss: 0.0902 - acc: 0.9677 - val_loss: 0.0837 - val_acc: 0.9690 Epoch 177/180 F1 Macro Score: 0.93782 - 8s - loss: 0.0910 - acc: 0.9676 - val_loss: 0.0837 - val_acc: 0.9690 Epoch 178/180 F1 Macro Score: 0.93779 - 7s - loss: 0.0904 - acc: 0.9677 - val_loss: 0.0837 - val_acc: 0.9690 Epoch 179/180 F1 Macro Score: 0.93777 - 8s - loss: 0.0902 - acc: 0.9677 - val_loss: 0.0837 - val_acc: 0.9690 Epoch 180/180 F1 Macro Score: 0.93774 - 8s - loss: 0.0901 - acc: 0.9677 - val_loss: 0.0837 - val_acc: 0.9690 Training fold 3 completed. macro f1 score : 0.93774 Our training dataset shape is (1000, 4000, 19) Our validation dataset shape is (250, 4000, 19) Train on 1000 samples, validate on 250 samples Epoch 1/180 F1 Macro Score: 0.73141 - 22s - loss: 0.5286 - acc: 0.8581 - val_loss: 0.7961 - val_acc: 0.8898 Epoch 2/180 F1 Macro Score: 0.80448 - 7s - loss: 0.1723 - acc: 0.9603 - val_loss: 0.4450 - val_acc: 0.9367 Epoch 3/180 F1 Macro Score: 0.90435 - 7s - loss: 0.1393 - acc: 0.9647 - val_loss: 0.2376 - val_acc: 0.9572 Epoch 4/180 F1 Macro Score: 0.92984 - 7s - loss: 0.1265 - acc: 0.9658 - val_loss: 0.1538 - val_acc: 0.9643 Epoch 5/180 F1 Macro Score: 0.92419 - 7s - loss: 0.1242 - acc: 0.9656 - val_loss: 0.1905 - val_acc: 0.9507 Epoch 6/180 F1 Macro Score: 0.93467 - 7s - loss: 0.1453 - acc: 0.9614 - val_loss: 0.1082 - val_acc: 0.9665 Epoch 7/180 F1 Macro Score: 0.93695 - 7s - loss: 0.1168 - acc: 0.9664 - val_loss: 0.1001 - val_acc: 0.9674 Epoch 8/180 F1 Macro Score: 0.93685 - 7s - loss: 0.1112 - acc: 0.9668 - val_loss: 0.0974 - val_acc: 0.9676 Epoch 9/180 F1 Macro Score: 0.93714 - 7s - loss: 0.1086 - acc: 0.9668 - val_loss: 0.0967 - val_acc: 0.9675 Epoch 10/180 F1 Macro Score: 0.93709 - 7s - loss: 0.1063 - acc: 0.9670 - val_loss: 0.0953 - val_acc: 0.9676 Epoch 11/180 F1 Macro Score: 0.93581 - 7s - loss: 0.1037 - acc: 0.9671 - val_loss: 0.0985 - val_acc: 0.9669 Epoch 12/180 F1 Macro Score: 0.93721 - 7s - loss: 0.1034 - acc: 0.9671 - val_loss: 0.0927 - val_acc: 0.9677 Epoch 13/180 F1 Macro Score: 0.93727 - 7s - loss: 0.1019 - acc: 0.9671 - val_loss: 0.0934 - val_acc: 0.9676 Epoch 14/180 F1 Macro Score: 0.93657 - 7s - loss: 0.1029 - acc: 0.9670 - val_loss: 0.0934 - val_acc: 0.9675 Epoch 15/180 F1 Macro Score: 0.93751 - 7s - loss: 0.1001 - acc: 0.9671 - val_loss: 0.0908 - val_acc: 0.9678 Epoch 16/180 F1 Macro Score: 0.93743 - 7s - loss: 0.0973 - acc: 0.9674 - val_loss: 0.0904 - val_acc: 0.9677 Epoch 17/180 F1 Macro Score: 0.93801 - 8s - loss: 0.0983 - acc: 0.9672 - val_loss: 0.0908 - val_acc: 0.9679 Epoch 18/180 F1 Macro Score: 0.93772 - 8s - loss: 0.0969 - acc: 0.9673 - val_loss: 0.0900 - val_acc: 0.9678 Epoch 19/180 F1 Macro Score: 0.93710 - 8s - loss: 0.0961 - acc: 0.9674 - val_loss: 0.0903 - val_acc: 0.9677 Epoch 20/180 F1 Macro Score: 0.93726 - 7s - loss: 0.0961 - acc: 0.9673 - val_loss: 0.0917 - val_acc: 0.9675 Epoch 21/180 F1 Macro Score: 0.93717 - 7s - loss: 0.0947 - acc: 0.9674 - val_loss: 0.0905 - val_acc: 0.9676 Epoch 22/180 F1 Macro Score: 0.93765 - 7s - loss: 0.0950 - acc: 0.9675 - val_loss: 0.0882 - val_acc: 0.9679 Epoch 23/180 F1 Macro Score: 0.93622 - 7s - loss: 0.0927 - acc: 0.9676 - val_loss: 0.0890 - val_acc: 0.9677 Epoch 24/180 F1 Macro Score: 0.93728 - 7s - loss: 0.0940 - acc: 0.9675 - val_loss: 0.0884 - val_acc: 0.9677 Epoch 25/180 F1 Macro Score: 0.93720 - 7s - loss: 0.0935 - acc: 0.9675 - val_loss: 0.0892 - val_acc: 0.9677 Epoch 26/180 F1 Macro Score: 0.93691 - 7s - loss: 0.0917 - acc: 0.9677 - val_loss: 0.0885 - val_acc: 0.9677 Epoch 27/180 F1 Macro Score: 0.93772 - 7s - loss: 0.0926 - acc: 0.9676 - val_loss: 0.0882 - val_acc: 0.9678 Epoch 28/180 F1 Macro Score: 0.93611 - 7s - loss: 0.0917 - acc: 0.9677 - val_loss: 0.0907 - val_acc: 0.9674 Epoch 29/180 F1 Macro Score: 0.93663 - 7s - loss: 0.0918 - acc: 0.9677 - val_loss: 0.0875 - val_acc: 0.9679 Epoch 30/180 F1 Macro Score: 0.93776 - 7s - loss: 0.0902 - acc: 0.9680 - val_loss: 0.0872 - val_acc: 0.9679 Epoch 31/180 F1 Macro Score: 0.93779 - 7s - loss: 0.0884 - acc: 0.9683 - val_loss: 0.0859 - val_acc: 0.9681 Epoch 32/180 F1 Macro Score: 0.93799 - 7s - loss: 0.0891 - acc: 0.9682 - val_loss: 0.0858 - val_acc: 0.9681 Epoch 33/180 F1 Macro Score: 0.93860 - 7s - loss: 0.0872 - acc: 0.9685 - val_loss: 0.0857 - val_acc: 0.9683 Epoch 34/180 F1 Macro Score: 0.93817 - 7s - loss: 0.0872 - acc: 0.9684 - val_loss: 0.0855 - val_acc: 0.9682 Epoch 35/180 F1 Macro Score: 0.93870 - 7s - loss: 0.0865 - acc: 0.9687 - val_loss: 0.0845 - val_acc: 0.9684 Epoch 36/180 F1 Macro Score: 0.93877 - 8s - loss: 0.0860 - acc: 0.9687 - val_loss: 0.0843 - val_acc: 0.9685 Epoch 37/180 F1 Macro Score: 0.93916 - 7s - loss: 0.0863 - acc: 0.9688 - val_loss: 0.0835 - val_acc: 0.9687 Epoch 38/180 F1 Macro Score: 0.93828 - 7s - loss: 0.0867 - acc: 0.9687 - val_loss: 0.0851 - val_acc: 0.9683 Epoch 39/180 F1 Macro Score: 0.93915 - 7s - loss: 0.0902 - acc: 0.9681 - val_loss: 0.0847 - val_acc: 0.9686 Epoch 40/180 F1 Macro Score: 0.93920 - 7s - loss: 0.0861 - acc: 0.9690 - val_loss: 0.0833 - val_acc: 0.9688 Epoch 41/180 F1 Macro Score: 0.93970 - 7s - loss: 0.0847 - acc: 0.9692 - val_loss: 0.0828 - val_acc: 0.9690 Epoch 42/180 F1 Macro Score: 0.93951 - 7s - loss: 0.0839 - acc: 0.9693 - val_loss: 0.0831 - val_acc: 0.9689 Epoch 43/180 F1 Macro Score: 0.93903 - 7s - loss: 0.0846 - acc: 0.9692 - val_loss: 0.0839 - val_acc: 0.9686 Epoch 44/180 F1 Macro Score: 0.94009 - 7s - loss: 0.0841 - acc: 0.9693 - val_loss: 0.0826 - val_acc: 0.9691 Epoch 45/180 F1 Macro Score: 0.93999 - 7s - loss: 0.0837 - acc: 0.9694 - val_loss: 0.0824 - val_acc: 0.9690 Epoch 46/180 F1 Macro Score: 0.93983 - 7s - loss: 0.0836 - acc: 0.9695 - val_loss: 0.0826 - val_acc: 0.9689 Epoch 47/180 F1 Macro Score: 0.93825 - 7s - loss: 0.0834 - acc: 0.9694 - val_loss: 0.0852 - val_acc: 0.9682 Epoch 48/180 F1 Macro Score: 0.93989 - 7s - loss: 0.0833 - acc: 0.9694 - val_loss: 0.0821 - val_acc: 0.9691 Epoch 49/180 F1 Macro Score: 0.93973 - 7s - loss: 0.0832 - acc: 0.9695 - val_loss: 0.0823 - val_acc: 0.9690 Epoch 50/180 F1 Macro Score: 0.93982 - 7s - loss: 0.0826 - acc: 0.9695 - val_loss: 0.0821 - val_acc: 0.9690 Epoch 51/180 F1 Macro Score: 0.94010 - 7s - loss: 0.0825 - acc: 0.9697 - val_loss: 0.0819 - val_acc: 0.9691 Epoch 52/180 F1 Macro Score: 0.94046 - 8s - loss: 0.0824 - acc: 0.9697 - val_loss: 0.0818 - val_acc: 0.9693 Epoch 53/180 F1 Macro Score: 0.93953 - 8s - loss: 0.0824 - acc: 0.9697 - val_loss: 0.0830 - val_acc: 0.9688 Epoch 54/180 F1 Macro Score: 0.93962 - 8s - loss: 0.0821 - acc: 0.9697 - val_loss: 0.0818 - val_acc: 0.9691 Epoch 55/180 F1 Macro Score: 0.94045 - 7s - loss: 0.0825 - acc: 0.9696 - val_loss: 0.0816 - val_acc: 0.9692 Epoch 56/180 F1 Macro Score: 0.93975 - 7s - loss: 0.0824 - acc: 0.9697 - val_loss: 0.0824 - val_acc: 0.9689 Epoch 57/180 F1 Macro Score: 0.93955 - 7s - loss: 0.0818 - acc: 0.9698 - val_loss: 0.0820 - val_acc: 0.9690 Epoch 58/180 F1 Macro Score: 0.94038 - 7s - loss: 0.0820 - acc: 0.9697 - val_loss: 0.0815 - val_acc: 0.9692 Epoch 59/180 F1 Macro Score: 0.93956 - 7s - loss: 0.0817 - acc: 0.9698 - val_loss: 0.0825 - val_acc: 0.9689 Epoch 60/180 F1 Macro Score: 0.94016 - 7s - loss: 0.0818 - acc: 0.9698 - val_loss: 0.0814 - val_acc: 0.9692 Epoch 61/180 F1 Macro Score: 0.94034 - 7s - loss: 0.0822 - acc: 0.9698 - val_loss: 0.0814 - val_acc: 0.9692 Epoch 62/180 F1 Macro Score: 0.94036 - 7s - loss: 0.0814 - acc: 0.9699 - val_loss: 0.0811 - val_acc: 0.9693 Epoch 63/180 F1 Macro Score: 0.94000 - 7s - loss: 0.0814 - acc: 0.9699 - val_loss: 0.0820 - val_acc: 0.9691 Epoch 64/180 F1 Macro Score: 0.94034 - 7s - loss: 0.0809 - acc: 0.9700 - val_loss: 0.0812 - val_acc: 0.9692 Epoch 65/180 F1 Macro Score: 0.94046 - 7s - loss: 0.0811 - acc: 0.9700 - val_loss: 0.0811 - val_acc: 0.9692 Epoch 66/180 F1 Macro Score: 0.93989 - 7s - loss: 0.0809 - acc: 0.9700 - val_loss: 0.0816 - val_acc: 0.9691 Epoch 67/180 F1 Macro Score: 0.94014 - 8s - loss: 0.0809 - acc: 0.9700 - val_loss: 0.0813 - val_acc: 0.9692 Epoch 68/180 F1 Macro Score: 0.94008 - 8s - loss: 0.0809 - acc: 0.9700 - val_loss: 0.0816 - val_acc: 0.9691 Epoch 69/180 F1 Macro Score: 0.93991 - 8s - loss: 0.0809 - acc: 0.9700 - val_loss: 0.0817 - val_acc: 0.9691 Epoch 70/180 F1 Macro Score: 0.94023 - 8s - loss: 0.0811 - acc: 0.9699 - val_loss: 0.0818 - val_acc: 0.9690 Epoch 71/180 F1 Macro Score: 0.94020 - 8s - loss: 0.0811 - acc: 0.9700 - val_loss: 0.0813 - val_acc: 0.9692 Epoch 72/180 F1 Macro Score: 0.94003 - 8s - loss: 0.0803 - acc: 0.9701 - val_loss: 0.0812 - val_acc: 0.9692 Epoch 73/180 F1 Macro Score: 0.93995 - 8s - loss: 0.0805 - acc: 0.9700 - val_loss: 0.0816 - val_acc: 0.9691 Epoch 74/180 F1 Macro Score: 0.93986 - 7s - loss: 0.0805 - acc: 0.9701 - val_loss: 0.0817 - val_acc: 0.9690 Epoch 75/180 F1 Macro Score: 0.94024 - 7s - loss: 0.0807 - acc: 0.9700 - val_loss: 0.0814 - val_acc: 0.9692 Epoch 76/180 F1 Macro Score: 0.94000 - 7s - loss: 0.0800 - acc: 0.9702 - val_loss: 0.0815 - val_acc: 0.9691 Epoch 77/180 F1 Macro Score: 0.94026 - 7s - loss: 0.0801 - acc: 0.9701 - val_loss: 0.0815 - val_acc: 0.9692 Epoch 78/180 F1 Macro Score: 0.93954 - 7s - loss: 0.0811 - acc: 0.9700 - val_loss: 0.0822 - val_acc: 0.9689 Epoch 79/180 F1 Macro Score: 0.94030 - 7s - loss: 0.0800 - acc: 0.9702 - val_loss: 0.0815 - val_acc: 0.9692 Epoch 80/180 F1 Macro Score: 0.93988 - 7s - loss: 0.0804 - acc: 0.9701 - val_loss: 0.0817 - val_acc: 0.9690 Epoch 81/180 F1 Macro Score: 0.94047 - 7s - loss: 0.0798 - acc: 0.9702 - val_loss: 0.0809 - val_acc: 0.9693 Epoch 82/180 F1 Macro Score: 0.94014 - 7s - loss: 0.0796 - acc: 0.9703 - val_loss: 0.0816 - val_acc: 0.9691 Epoch 83/180 F1 Macro Score: 0.94025 - 7s - loss: 0.0797 - acc: 0.9703 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 84/180 F1 Macro Score: 0.94044 - 7s - loss: 0.0802 - acc: 0.9702 - val_loss: 0.0816 - val_acc: 0.9692 Epoch 85/180 F1 Macro Score: 0.94026 - 8s - loss: 0.0794 - acc: 0.9703 - val_loss: 0.0811 - val_acc: 0.9693 Epoch 86/180 F1 Macro Score: 0.94014 - 7s - loss: 0.0794 - acc: 0.9703 - val_loss: 0.0812 - val_acc: 0.9692 Epoch 87/180 F1 Macro Score: 0.94029 - 7s - loss: 0.0792 - acc: 0.9704 - val_loss: 0.0812 - val_acc: 0.9693 Epoch 88/180 F1 Macro Score: 0.94015 - 7s - loss: 0.0792 - acc: 0.9704 - val_loss: 0.0812 - val_acc: 0.9692 Epoch 89/180 F1 Macro Score: 0.94034 - 8s - loss: 0.0798 - acc: 0.9703 - val_loss: 0.0810 - val_acc: 0.9692 Epoch 90/180 F1 Macro Score: 0.94012 - 7s - loss: 0.0792 - acc: 0.9704 - val_loss: 0.0813 - val_acc: 0.9691 Epoch 91/180 F1 Macro Score: 0.94024 - 7s - loss: 0.0788 - acc: 0.9705 - val_loss: 0.0807 - val_acc: 0.9692 Epoch 92/180 F1 Macro Score: 0.94023 - 7s - loss: 0.0789 - acc: 0.9705 - val_loss: 0.0807 - val_acc: 0.9692 Epoch 93/180 F1 Macro Score: 0.94022 - 8s - loss: 0.0787 - acc: 0.9706 - val_loss: 0.0807 - val_acc: 0.9693 Epoch 94/180 F1 Macro Score: 0.94032 - 7s - loss: 0.0795 - acc: 0.9704 - val_loss: 0.0808 - val_acc: 0.9693 Epoch 95/180 F1 Macro Score: 0.94025 - 7s - loss: 0.0787 - acc: 0.9705 - val_loss: 0.0808 - val_acc: 0.9693 Epoch 96/180 F1 Macro Score: 0.94037 - 7s - loss: 0.0783 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9693 Epoch 97/180 F1 Macro Score: 0.94032 - 7s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0807 - val_acc: 0.9693 Epoch 98/180 F1 Macro Score: 0.94031 - 7s - loss: 0.0791 - acc: 0.9705 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 99/180 F1 Macro Score: 0.94026 - 7s - loss: 0.0787 - acc: 0.9706 - val_loss: 0.0807 - val_acc: 0.9692 Epoch 100/180 F1 Macro Score: 0.94037 - 8s - loss: 0.0787 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9693 Epoch 101/180 F1 Macro Score: 0.94033 - 8s - loss: 0.0789 - acc: 0.9705 - val_loss: 0.0808 - val_acc: 0.9693 Epoch 102/180 F1 Macro Score: 0.94013 - 8s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 103/180 F1 Macro Score: 0.94023 - 8s - loss: 0.0785 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9693 Epoch 104/180 F1 Macro Score: 0.94018 - 8s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 105/180 F1 Macro Score: 0.94026 - 8s - loss: 0.0787 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 106/180 F1 Macro Score: 0.94032 - 8s - loss: 0.0786 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9693 Epoch 107/180 F1 Macro Score: 0.94017 - 8s - loss: 0.0790 - acc: 0.9705 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 108/180 F1 Macro Score: 0.94017 - 8s - loss: 0.0790 - acc: 0.9706 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 109/180 F1 Macro Score: 0.94033 - 8s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9693 Epoch 110/180 F1 Macro Score: 0.94033 - 8s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 111/180 F1 Macro Score: 0.94027 - 8s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0807 - val_acc: 0.9692 Epoch 112/180 F1 Macro Score: 0.94022 - 8s - loss: 0.0786 - acc: 0.9705 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 113/180 F1 Macro Score: 0.94027 - 8s - loss: 0.0787 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 114/180 F1 Macro Score: 0.94011 - 8s - loss: 0.0782 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 115/180 F1 Macro Score: 0.94013 - 8s - loss: 0.0783 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 116/180 F1 Macro Score: 0.94027 - 8s - loss: 0.0785 - acc: 0.9706 - val_loss: 0.0807 - val_acc: 0.9693 Epoch 117/180 F1 Macro Score: 0.94011 - 8s - loss: 0.0782 - acc: 0.9706 - val_loss: 0.0810 - val_acc: 0.9692 Epoch 118/180 F1 Macro Score: 0.94036 - 8s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0809 - val_acc: 0.9693 Epoch 119/180 F1 Macro Score: 0.94019 - 7s - loss: 0.0786 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 120/180 F1 Macro Score: 0.94011 - 7s - loss: 0.0783 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 121/180 F1 Macro Score: 0.94006 - 8s - loss: 0.0790 - acc: 0.9705 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 122/180 F1 Macro Score: 0.94025 - 7s - loss: 0.0785 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 123/180 F1 Macro Score: 0.94020 - 7s - loss: 0.0782 - acc: 0.9706 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 124/180 F1 Macro Score: 0.94010 - 7s - loss: 0.0785 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 125/180 F1 Macro Score: 0.94025 - 7s - loss: 0.0783 - acc: 0.9707 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 126/180 F1 Macro Score: 0.94024 - 7s - loss: 0.0783 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 127/180 F1 Macro Score: 0.94040 - 7s - loss: 0.0786 - acc: 0.9706 - val_loss: 0.0809 - val_acc: 0.9693 Epoch 128/180 F1 Macro Score: 0.94012 - 7s - loss: 0.0783 - acc: 0.9706 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 129/180 F1 Macro Score: 0.94035 - 7s - loss: 0.0783 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 130/180 F1 Macro Score: 0.94014 - 7s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 131/180 F1 Macro Score: 0.94025 - 7s - loss: 0.0780 - acc: 0.9707 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 132/180 F1 Macro Score: 0.94023 - 7s - loss: 0.0787 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 133/180 F1 Macro Score: 0.94039 - 7s - loss: 0.0782 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9693 Epoch 134/180 F1 Macro Score: 0.94004 - 7s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0810 - val_acc: 0.9692 Epoch 135/180 F1 Macro Score: 0.94012 - 7s - loss: 0.0786 - acc: 0.9706 - val_loss: 0.0810 - val_acc: 0.9691 Epoch 136/180 F1 Macro Score: 0.94011 - 7s - loss: 0.0787 - acc: 0.9705 - val_loss: 0.0811 - val_acc: 0.9691 Epoch 137/180 F1 Macro Score: 0.94019 - 7s - loss: 0.0780 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 138/180 F1 Macro Score: 0.94010 - 7s - loss: 0.0782 - acc: 0.9706 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 139/180 F1 Macro Score: 0.94037 - 7s - loss: 0.0781 - acc: 0.9707 - val_loss: 0.0808 - val_acc: 0.9693 Epoch 140/180 F1 Macro Score: 0.94020 - 7s - loss: 0.0783 - acc: 0.9707 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 141/180 F1 Macro Score: 0.94019 - 7s - loss: 0.0786 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 142/180 F1 Macro Score: 0.94011 - 7s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 143/180 F1 Macro Score: 0.94006 - 7s - loss: 0.0782 - acc: 0.9707 - val_loss: 0.0810 - val_acc: 0.9692 Epoch 144/180 F1 Macro Score: 0.94008 - 7s - loss: 0.0785 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9691 Epoch 145/180 F1 Macro Score: 0.94027 - 7s - loss: 0.0780 - acc: 0.9707 - val_loss: 0.0810 - val_acc: 0.9692 Epoch 146/180 F1 Macro Score: 0.94023 - 7s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0810 - val_acc: 0.9692 Epoch 147/180 F1 Macro Score: 0.94019 - 7s - loss: 0.0780 - acc: 0.9707 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 148/180 F1 Macro Score: 0.94025 - 7s - loss: 0.0784 - acc: 0.9705 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 149/180 F1 Macro Score: 0.94027 - 7s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 150/180 F1 Macro Score: 0.94010 - 7s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 151/180 F1 Macro Score: 0.94027 - 8s - loss: 0.0781 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 152/180 F1 Macro Score: 0.94008 - 8s - loss: 0.0781 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 153/180 F1 Macro Score: 0.94021 - 8s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 154/180 F1 Macro Score: 0.94012 - 8s - loss: 0.0781 - acc: 0.9706 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 155/180 F1 Macro Score: 0.94017 - 8s - loss: 0.0779 - acc: 0.9708 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 156/180 F1 Macro Score: 0.94017 - 8s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 157/180 F1 Macro Score: 0.94023 - 8s - loss: 0.0781 - acc: 0.9706 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 158/180 F1 Macro Score: 0.94017 - 8s - loss: 0.0786 - acc: 0.9706 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 159/180 F1 Macro Score: 0.94005 - 8s - loss: 0.0778 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 160/180 F1 Macro Score: 0.94012 - 8s - loss: 0.0784 - acc: 0.9706 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 161/180 F1 Macro Score: 0.94019 - 8s - loss: 0.0782 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 162/180 F1 Macro Score: 0.94026 - 8s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 163/180 F1 Macro Score: 0.94007 - 8s - loss: 0.0778 - acc: 0.9708 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 164/180 F1 Macro Score: 0.94017 - 8s - loss: 0.0778 - acc: 0.9708 - val_loss: 0.0812 - val_acc: 0.9692 Epoch 165/180 F1 Macro Score: 0.94009 - 8s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0810 - val_acc: 0.9692 Epoch 166/180 F1 Macro Score: 0.94025 - 8s - loss: 0.0778 - acc: 0.9708 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 167/180 F1 Macro Score: 0.94019 - 8s - loss: 0.0780 - acc: 0.9706 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 168/180 F1 Macro Score: 0.94021 - 7s - loss: 0.0778 - acc: 0.9708 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 169/180 F1 Macro Score: 0.94027 - 7s - loss: 0.0775 - acc: 0.9708 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 170/180 F1 Macro Score: 0.94020 - 7s - loss: 0.0778 - acc: 0.9708 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 171/180 F1 Macro Score: 0.93992 - 7s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0810 - val_acc: 0.9691 Epoch 172/180 F1 Macro Score: 0.94002 - 7s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9691 Epoch 173/180 F1 Macro Score: 0.94020 - 7s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 174/180 F1 Macro Score: 0.94020 - 7s - loss: 0.0777 - acc: 0.9708 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 175/180 F1 Macro Score: 0.94004 - 7s - loss: 0.0778 - acc: 0.9708 - val_loss: 0.0810 - val_acc: 0.9691 Epoch 176/180 F1 Macro Score: 0.94011 - 7s - loss: 0.0777 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 177/180 F1 Macro Score: 0.94015 - 7s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 178/180 F1 Macro Score: 0.94019 - 7s - loss: 0.0776 - acc: 0.9708 - val_loss: 0.0808 - val_acc: 0.9692 Epoch 179/180 F1 Macro Score: 0.94028 - 7s - loss: 0.0780 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Epoch 180/180 F1 Macro Score: 0.94027 - 7s - loss: 0.0777 - acc: 0.9707 - val_loss: 0.0809 - val_acc: 0.9692 Training fold 4 completed. macro f1 score : 0.94027 Our training dataset shape is (1000, 4000, 19) Our validation dataset shape is (250, 4000, 19) Train on 1000 samples, validate on 250 samples Epoch 1/180 F1 Macro Score: 0.72154 - 24s - loss: 0.5772 - acc: 0.8375 - val_loss: 0.9280 - val_acc: 0.8896 Epoch 2/180 F1 Macro Score: 0.91849 - 8s - loss: 0.1851 - acc: 0.9583 - val_loss: 0.4552 - val_acc: 0.9598 Epoch 3/180 F1 Macro Score: 0.93262 - 8s - loss: 0.1425 - acc: 0.9641 - val_loss: 0.2407 - val_acc: 0.9657 Epoch 4/180 F1 Macro Score: 0.93494 - 8s - loss: 0.1270 - acc: 0.9658 - val_loss: 0.1442 - val_acc: 0.9664 Epoch 5/180 F1 Macro Score: 0.93494 - 8s - loss: 0.1200 - acc: 0.9662 - val_loss: 0.1215 - val_acc: 0.9663 Epoch 6/180 F1 Macro Score: 0.93678 - 8s - loss: 0.1174 - acc: 0.9662 - val_loss: 0.1030 - val_acc: 0.9674 Epoch 7/180 F1 Macro Score: 0.93727 - 8s - loss: 0.1128 - acc: 0.9666 - val_loss: 0.0981 - val_acc: 0.9676 Epoch 8/180 F1 Macro Score: 0.93544 - 8s - loss: 0.1108 - acc: 0.9666 - val_loss: 0.1025 - val_acc: 0.9666 Epoch 9/180 F1 Macro Score: 0.93726 - 8s - loss: 0.1083 - acc: 0.9668 - val_loss: 0.0947 - val_acc: 0.9676 Epoch 10/180 F1 Macro Score: 0.93630 - 8s - loss: 0.1061 - acc: 0.9669 - val_loss: 0.0963 - val_acc: 0.9672 Epoch 11/180 F1 Macro Score: 0.93748 - 8s - loss: 0.1035 - acc: 0.9670 - val_loss: 0.0919 - val_acc: 0.9678 Epoch 12/180 F1 Macro Score: 0.93715 - 8s - loss: 0.1033 - acc: 0.9670 - val_loss: 0.0936 - val_acc: 0.9675 Epoch 13/180 F1 Macro Score: 0.93762 - 8s - loss: 0.1010 - acc: 0.9670 - val_loss: 0.0909 - val_acc: 0.9678 Epoch 14/180 F1 Macro Score: 0.93618 - 8s - loss: 0.1003 - acc: 0.9670 - val_loss: 0.0924 - val_acc: 0.9675 Epoch 15/180 F1 Macro Score: 0.93663 - 8s - loss: 0.1028 - acc: 0.9669 - val_loss: 0.0925 - val_acc: 0.9675 Epoch 16/180 F1 Macro Score: 0.93657 - 7s - loss: 0.1003 - acc: 0.9670 - val_loss: 0.0923 - val_acc: 0.9673 Epoch 17/180 F1 Macro Score: 0.93615 - 7s - loss: 0.0980 - acc: 0.9671 - val_loss: 0.0935 - val_acc: 0.9672 Epoch 18/180 F1 Macro Score: 0.93763 - 8s - loss: 0.0979 - acc: 0.9671 - val_loss: 0.0886 - val_acc: 0.9679 Epoch 19/180 F1 Macro Score: 0.85357 - 8s - loss: 0.1268 - acc: 0.9609 - val_loss: 0.2296 - val_acc: 0.9320 Epoch 20/180 F1 Macro Score: 0.93335 - 8s - loss: 0.1287 - acc: 0.9632 - val_loss: 0.1158 - val_acc: 0.9662 Epoch 21/180 F1 Macro Score: 0.93648 - 8s - loss: 0.1062 - acc: 0.9668 - val_loss: 0.0943 - val_acc: 0.9677 Epoch 22/180 F1 Macro Score: 0.93706 - 8s - loss: 0.1001 - acc: 0.9672 - val_loss: 0.0918 - val_acc: 0.9677 Epoch 23/180 F1 Macro Score: 0.93643 - 8s - loss: 0.0990 - acc: 0.9672 - val_loss: 0.0910 - val_acc: 0.9676 Epoch 24/180 F1 Macro Score: 0.93716 - 7s - loss: 0.0977 - acc: 0.9672 - val_loss: 0.0898 - val_acc: 0.9677 Epoch 25/180 F1 Macro Score: 0.93605 - 7s - loss: 0.0983 - acc: 0.9671 - val_loss: 0.0916 - val_acc: 0.9673 Epoch 26/180 F1 Macro Score: 0.93608 - 8s - loss: 0.0957 - acc: 0.9674 - val_loss: 0.0890 - val_acc: 0.9677 Epoch 27/180 F1 Macro Score: 0.93690 - 8s - loss: 0.0939 - acc: 0.9675 - val_loss: 0.0885 - val_acc: 0.9677 Epoch 28/180 F1 Macro Score: 0.93605 - 7s - loss: 0.0962 - acc: 0.9672 - val_loss: 0.0895 - val_acc: 0.9675 Epoch 29/180 F1 Macro Score: 0.93706 - 9s - loss: 0.0937 - acc: 0.9675 - val_loss: 0.0884 - val_acc: 0.9677 Epoch 30/180 F1 Macro Score: 0.93769 - 8s - loss: 0.0926 - acc: 0.9675 - val_loss: 0.0871 - val_acc: 0.9680 Epoch 31/180 F1 Macro Score: 0.93761 - 8s - loss: 0.0910 - acc: 0.9678 - val_loss: 0.0867 - val_acc: 0.9679 Epoch 32/180 F1 Macro Score: 0.93817 - 8s - loss: 0.0905 - acc: 0.9678 - val_loss: 0.0856 - val_acc: 0.9682 Epoch 33/180 F1 Macro Score: 0.93796 - 8s - loss: 0.0903 - acc: 0.9678 - val_loss: 0.0854 - val_acc: 0.9682 Epoch 34/180 F1 Macro Score: 0.93814 - 8s - loss: 0.0900 - acc: 0.9678 - val_loss: 0.0850 - val_acc: 0.9683 Epoch 35/180 F1 Macro Score: 0.93807 - 8s - loss: 0.0898 - acc: 0.9679 - val_loss: 0.0849 - val_acc: 0.9682 Epoch 36/180 F1 Macro Score: 0.93776 - 8s - loss: 0.0898 - acc: 0.9679 - val_loss: 0.0857 - val_acc: 0.9681 Epoch 37/180 F1 Macro Score: 0.93823 - 8s - loss: 0.0891 - acc: 0.9680 - val_loss: 0.0844 - val_acc: 0.9683 Epoch 38/180 F1 Macro Score: 0.93833 - 8s - loss: 0.0890 - acc: 0.9680 - val_loss: 0.0845 - val_acc: 0.9683 Epoch 39/180 F1 Macro Score: 0.93789 - 8s - loss: 0.0887 - acc: 0.9680 - val_loss: 0.0847 - val_acc: 0.9683 Epoch 40/180 F1 Macro Score: 0.93774 - 8s - loss: 0.0889 - acc: 0.9680 - val_loss: 0.0848 - val_acc: 0.9683 Epoch 41/180 F1 Macro Score: 0.93822 - 8s - loss: 0.0891 - acc: 0.9680 - val_loss: 0.0844 - val_acc: 0.9683 Epoch 42/180 F1 Macro Score: 0.93842 - 8s - loss: 0.0879 - acc: 0.9682 - val_loss: 0.0838 - val_acc: 0.9684 Epoch 43/180 F1 Macro Score: 0.93841 - 8s - loss: 0.0883 - acc: 0.9680 - val_loss: 0.0838 - val_acc: 0.9685 Epoch 44/180 F1 Macro Score: 0.93838 - 7s - loss: 0.0883 - acc: 0.9681 - val_loss: 0.0839 - val_acc: 0.9684 Epoch 45/180 F1 Macro Score: 0.93821 - 8s - loss: 0.0878 - acc: 0.9683 - val_loss: 0.0838 - val_acc: 0.9684 Epoch 46/180 F1 Macro Score: 0.93795 - 8s - loss: 0.0879 - acc: 0.9682 - val_loss: 0.0841 - val_acc: 0.9683 Epoch 47/180 F1 Macro Score: 0.93858 - 8s - loss: 0.0878 - acc: 0.9682 - val_loss: 0.0839 - val_acc: 0.9684 Epoch 48/180 F1 Macro Score: 0.93842 - 8s - loss: 0.0874 - acc: 0.9682 - val_loss: 0.0836 - val_acc: 0.9685 Epoch 49/180 F1 Macro Score: 0.93859 - 7s - loss: 0.0872 - acc: 0.9683 - val_loss: 0.0837 - val_acc: 0.9685 Epoch 50/180 F1 Macro Score: 0.93839 - 8s - loss: 0.0872 - acc: 0.9683 - val_loss: 0.0835 - val_acc: 0.9684 Epoch 51/180 F1 Macro Score: 0.93863 - 8s - loss: 0.0871 - acc: 0.9683 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 52/180 F1 Macro Score: 0.93873 - 8s - loss: 0.0865 - acc: 0.9685 - val_loss: 0.0830 - val_acc: 0.9686 Epoch 53/180 F1 Macro Score: 0.93885 - 8s - loss: 0.0864 - acc: 0.9685 - val_loss: 0.0830 - val_acc: 0.9686 Epoch 54/180 F1 Macro Score: 0.93827 - 8s - loss: 0.0861 - acc: 0.9685 - val_loss: 0.0834 - val_acc: 0.9685 Epoch 55/180 F1 Macro Score: 0.93875 - 8s - loss: 0.0862 - acc: 0.9685 - val_loss: 0.0829 - val_acc: 0.9686 Epoch 56/180 F1 Macro Score: 0.93862 - 8s - loss: 0.0860 - acc: 0.9686 - val_loss: 0.0831 - val_acc: 0.9685 Epoch 57/180 F1 Macro Score: 0.93887 - 8s - loss: 0.0863 - acc: 0.9685 - val_loss: 0.0831 - val_acc: 0.9686 Epoch 58/180 F1 Macro Score: 0.93925 - 8s - loss: 0.0857 - acc: 0.9686 - val_loss: 0.0828 - val_acc: 0.9687 Epoch 59/180 F1 Macro Score: 0.93867 - 8s - loss: 0.0855 - acc: 0.9687 - val_loss: 0.0827 - val_acc: 0.9686 Epoch 60/180 F1 Macro Score: 0.93929 - 8s - loss: 0.0852 - acc: 0.9687 - val_loss: 0.0828 - val_acc: 0.9688 Epoch 61/180 F1 Macro Score: 0.93924 - 8s - loss: 0.0853 - acc: 0.9688 - val_loss: 0.0821 - val_acc: 0.9689 Epoch 62/180 F1 Macro Score: 0.93958 - 8s - loss: 0.0848 - acc: 0.9689 - val_loss: 0.0821 - val_acc: 0.9690 Epoch 63/180 F1 Macro Score: 0.93846 - 8s - loss: 0.0852 - acc: 0.9689 - val_loss: 0.0827 - val_acc: 0.9686 Epoch 64/180 F1 Macro Score: 0.93968 - 7s - loss: 0.0860 - acc: 0.9687 - val_loss: 0.0820 - val_acc: 0.9691 Epoch 65/180 F1 Macro Score: 0.93931 - 8s - loss: 0.0866 - acc: 0.9686 - val_loss: 0.0824 - val_acc: 0.9689 Epoch 66/180 F1 Macro Score: 0.93918 - 8s - loss: 0.0847 - acc: 0.9690 - val_loss: 0.0821 - val_acc: 0.9690 Epoch 67/180 F1 Macro Score: 0.93950 - 8s - loss: 0.0839 - acc: 0.9691 - val_loss: 0.0819 - val_acc: 0.9690 Epoch 68/180 F1 Macro Score: 0.94007 - 8s - loss: 0.0842 - acc: 0.9691 - val_loss: 0.0813 - val_acc: 0.9692 Epoch 69/180 F1 Macro Score: 0.93934 - 8s - loss: 0.0839 - acc: 0.9692 - val_loss: 0.0815 - val_acc: 0.9691 Epoch 70/180 F1 Macro Score: 0.94007 - 8s - loss: 0.0837 - acc: 0.9693 - val_loss: 0.0816 - val_acc: 0.9692 Epoch 71/180 F1 Macro Score: 0.93993 - 8s - loss: 0.0834 - acc: 0.9693 - val_loss: 0.0813 - val_acc: 0.9692 Epoch 72/180 F1 Macro Score: 0.93994 - 8s - loss: 0.0845 - acc: 0.9691 - val_loss: 0.0813 - val_acc: 0.9691 Epoch 73/180 F1 Macro Score: 0.94001 - 7s - loss: 0.0831 - acc: 0.9694 - val_loss: 0.0810 - val_acc: 0.9693 Epoch 74/180 F1 Macro Score: 0.93960 - 8s - loss: 0.0840 - acc: 0.9693 - val_loss: 0.0828 - val_acc: 0.9690 Epoch 75/180 F1 Macro Score: 0.93991 - 8s - loss: 0.0841 - acc: 0.9692 - val_loss: 0.0816 - val_acc: 0.9692 Epoch 76/180 F1 Macro Score: 0.94036 - 8s - loss: 0.0834 - acc: 0.9694 - val_loss: 0.0810 - val_acc: 0.9693 Epoch 77/180 F1 Macro Score: 0.94039 - 8s - loss: 0.0828 - acc: 0.9695 - val_loss: 0.0811 - val_acc: 0.9694 Epoch 78/180 F1 Macro Score: 0.94008 - 8s - loss: 0.0834 - acc: 0.9694 - val_loss: 0.0808 - val_acc: 0.9693 Epoch 79/180 F1 Macro Score: 0.94026 - 8s - loss: 0.0830 - acc: 0.9695 - val_loss: 0.0805 - val_acc: 0.9694 Epoch 80/180 F1 Macro Score: 0.94054 - 8s - loss: 0.0829 - acc: 0.9696 - val_loss: 0.0805 - val_acc: 0.9695 Epoch 81/180 F1 Macro Score: 0.94068 - 8s - loss: 0.0825 - acc: 0.9696 - val_loss: 0.0803 - val_acc: 0.9695 Epoch 82/180 F1 Macro Score: 0.94034 - 8s - loss: 0.0821 - acc: 0.9696 - val_loss: 0.0805 - val_acc: 0.9694 Epoch 83/180 F1 Macro Score: 0.94063 - 8s - loss: 0.0822 - acc: 0.9697 - val_loss: 0.0804 - val_acc: 0.9695 Epoch 84/180 F1 Macro Score: 0.94031 - 8s - loss: 0.0822 - acc: 0.9696 - val_loss: 0.0809 - val_acc: 0.9694 Epoch 85/180 F1 Macro Score: 0.94041 - 8s - loss: 0.0831 - acc: 0.9695 - val_loss: 0.0806 - val_acc: 0.9695 Epoch 86/180 F1 Macro Score: 0.93967 - 8s - loss: 0.0823 - acc: 0.9696 - val_loss: 0.0811 - val_acc: 0.9692 Epoch 87/180 F1 Macro Score: 0.94047 - 8s - loss: 0.0817 - acc: 0.9697 - val_loss: 0.0806 - val_acc: 0.9695 Epoch 88/180 F1 Macro Score: 0.94054 - 8s - loss: 0.0816 - acc: 0.9698 - val_loss: 0.0807 - val_acc: 0.9695 Epoch 89/180 F1 Macro Score: 0.94052 - 8s - loss: 0.0816 - acc: 0.9698 - val_loss: 0.0805 - val_acc: 0.9694 Epoch 90/180 F1 Macro Score: 0.94078 - 8s - loss: 0.0823 - acc: 0.9697 - val_loss: 0.0799 - val_acc: 0.9696 Epoch 91/180 F1 Macro Score: 0.94100 - 8s - loss: 0.0812 - acc: 0.9699 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 92/180 F1 Macro Score: 0.94095 - 8s - loss: 0.0813 - acc: 0.9699 - val_loss: 0.0798 - val_acc: 0.9697 Epoch 93/180 F1 Macro Score: 0.94085 - 8s - loss: 0.0812 - acc: 0.9699 - val_loss: 0.0798 - val_acc: 0.9696 Epoch 94/180 F1 Macro Score: 0.94101 - 8s - loss: 0.0813 - acc: 0.9699 - val_loss: 0.0798 - val_acc: 0.9697 Epoch 95/180 F1 Macro Score: 0.94095 - 8s - loss: 0.0811 - acc: 0.9699 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 96/180 F1 Macro Score: 0.94096 - 8s - loss: 0.0809 - acc: 0.9700 - val_loss: 0.0798 - val_acc: 0.9697 Epoch 97/180 F1 Macro Score: 0.94091 - 8s - loss: 0.0826 - acc: 0.9696 - val_loss: 0.0798 - val_acc: 0.9697 Epoch 98/180 F1 Macro Score: 0.94090 - 8s - loss: 0.0816 - acc: 0.9698 - val_loss: 0.0798 - val_acc: 0.9697 Epoch 99/180 F1 Macro Score: 0.94093 - 8s - loss: 0.0811 - acc: 0.9699 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 100/180 F1 Macro Score: 0.94096 - 8s - loss: 0.0809 - acc: 0.9699 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 101/180 F1 Macro Score: 0.94097 - 8s - loss: 0.0811 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 102/180 F1 Macro Score: 0.94100 - 8s - loss: 0.0809 - acc: 0.9699 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 103/180 F1 Macro Score: 0.94090 - 8s - loss: 0.0820 - acc: 0.9697 - val_loss: 0.0797 - val_acc: 0.9696 Epoch 104/180 F1 Macro Score: 0.94096 - 8s - loss: 0.0811 - acc: 0.9699 - val_loss: 0.0798 - val_acc: 0.9697 Epoch 105/180 F1 Macro Score: 0.94091 - 8s - loss: 0.0809 - acc: 0.9699 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 106/180 F1 Macro Score: 0.94084 - 8s - loss: 0.0807 - acc: 0.9699 - val_loss: 0.0797 - val_acc: 0.9696 Epoch 107/180 F1 Macro Score: 0.94097 - 8s - loss: 0.0809 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 108/180 F1 Macro Score: 0.94089 - 8s - loss: 0.0809 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 109/180 F1 Macro Score: 0.94091 - 8s - loss: 0.0807 - acc: 0.9699 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 110/180 F1 Macro Score: 0.94087 - 8s - loss: 0.0812 - acc: 0.9699 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 111/180 F1 Macro Score: 0.94091 - 8s - loss: 0.0806 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 112/180 F1 Macro Score: 0.94097 - 8s - loss: 0.0808 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 113/180 F1 Macro Score: 0.94099 - 8s - loss: 0.0804 - acc: 0.9701 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 114/180 F1 Macro Score: 0.94091 - 8s - loss: 0.0809 - acc: 0.9699 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 115/180 F1 Macro Score: 0.94086 - 8s - loss: 0.0808 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 116/180 F1 Macro Score: 0.94089 - 7s - loss: 0.0810 - acc: 0.9699 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 117/180 F1 Macro Score: 0.94090 - 8s - loss: 0.0815 - acc: 0.9699 - val_loss: 0.0797 - val_acc: 0.9696 Epoch 118/180 F1 Macro Score: 0.94085 - 8s - loss: 0.0805 - acc: 0.9701 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 119/180 F1 Macro Score: 0.94086 - 8s - loss: 0.0806 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 120/180 F1 Macro Score: 0.94079 - 8s - loss: 0.0810 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9696 Epoch 121/180 F1 Macro Score: 0.94087 - 8s - loss: 0.0806 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9696 Epoch 122/180 F1 Macro Score: 0.94092 - 8s - loss: 0.0807 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 123/180 F1 Macro Score: 0.94087 - 8s - loss: 0.0820 - acc: 0.9698 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 124/180 F1 Macro Score: 0.94096 - 8s - loss: 0.0811 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 125/180 F1 Macro Score: 0.94091 - 8s - loss: 0.0809 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 126/180 F1 Macro Score: 0.94103 - 8s - loss: 0.0807 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 127/180 F1 Macro Score: 0.94097 - 8s - loss: 0.0807 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 128/180 F1 Macro Score: 0.94102 - 8s - loss: 0.0804 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 129/180 F1 Macro Score: 0.94082 - 8s - loss: 0.0809 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9696 Epoch 130/180 F1 Macro Score: 0.94092 - 8s - loss: 0.0806 - acc: 0.9699 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 131/180 F1 Macro Score: 0.94093 - 8s - loss: 0.0806 - acc: 0.9700 - val_loss: 0.0797 - val_acc: 0.9697 Epoch 132/180 F1 Macro Score: 0.94091 - 8s - loss: 0.0805 - acc: 0.9701 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 133/180 F1 Macro Score: 0.94095 - 8s - loss: 0.0805 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 134/180 F1 Macro Score: 0.94094 - 8s - loss: 0.0805 - acc: 0.9701 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 135/180 F1 Macro Score: 0.94094 - 8s - loss: 0.0809 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 136/180 F1 Macro Score: 0.94107 - 7s - loss: 0.0804 - acc: 0.9701 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 137/180 F1 Macro Score: 0.94094 - 8s - loss: 0.0808 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 138/180 F1 Macro Score: 0.94100 - 8s - loss: 0.0802 - acc: 0.9701 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 139/180 F1 Macro Score: 0.94086 - 8s - loss: 0.0808 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 140/180 F1 Macro Score: 0.94098 - 8s - loss: 0.0804 - acc: 0.9700 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 141/180 F1 Macro Score: 0.94100 - 8s - loss: 0.0807 - acc: 0.9699 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 142/180 F1 Macro Score: 0.94094 - 8s - loss: 0.0805 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 143/180 F1 Macro Score: 0.94094 - 8s - loss: 0.0805 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 144/180 F1 Macro Score: 0.94086 - 8s - loss: 0.0803 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9696 Epoch 145/180 F1 Macro Score: 0.94091 - 8s - loss: 0.0805 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9696 Epoch 146/180 F1 Macro Score: 0.94099 - 8s - loss: 0.0803 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 147/180 F1 Macro Score: 0.94091 - 8s - loss: 0.0804 - acc: 0.9701 - val_loss: 0.0795 - val_acc: 0.9696 Epoch 148/180 F1 Macro Score: 0.94089 - 7s - loss: 0.0807 - acc: 0.9700 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 149/180 F1 Macro Score: 0.94094 - 8s - loss: 0.0801 - acc: 0.9701 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 150/180 F1 Macro Score: 0.94107 - 8s - loss: 0.0806 - acc: 0.9700 - val_loss: 0.0794 - val_acc: 0.9697 Epoch 151/180 F1 Macro Score: 0.94091 - 8s - loss: 0.0806 - acc: 0.9700 - val_loss: 0.0795 - val_acc: 0.9696 Epoch 152/180 F1 Macro Score: 0.94095 - 8s - loss: 0.0806 - acc: 0.9700 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 153/180 F1 Macro Score: 0.94093 - 7s - loss: 0.0806 - acc: 0.9700 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 154/180 F1 Macro Score: 0.94099 - 8s - loss: 0.0801 - acc: 0.9701 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 155/180 F1 Macro Score: 0.94103 - 9s - loss: 0.0805 - acc: 0.9700 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 156/180 F1 Macro Score: 0.94099 - 8s - loss: 0.0805 - acc: 0.9700 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 157/180 F1 Macro Score: 0.94098 - 8s - loss: 0.0801 - acc: 0.9702 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 158/180 F1 Macro Score: 0.94094 - 8s - loss: 0.0801 - acc: 0.9701 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 159/180 F1 Macro Score: 0.94101 - 8s - loss: 0.0799 - acc: 0.9702 - val_loss: 0.0794 - val_acc: 0.9697 Epoch 160/180 F1 Macro Score: 0.94101 - 8s - loss: 0.0802 - acc: 0.9702 - val_loss: 0.0794 - val_acc: 0.9697 Epoch 161/180 F1 Macro Score: 0.94106 - 8s - loss: 0.0805 - acc: 0.9700 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 162/180 F1 Macro Score: 0.94100 - 8s - loss: 0.0805 - acc: 0.9701 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 163/180 F1 Macro Score: 0.94101 - 8s - loss: 0.0800 - acc: 0.9701 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 164/180 F1 Macro Score: 0.94100 - 9s - loss: 0.0802 - acc: 0.9701 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 165/180 F1 Macro Score: 0.94097 - 9s - loss: 0.0800 - acc: 0.9702 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 166/180 F1 Macro Score: 0.94102 - 8s - loss: 0.0803 - acc: 0.9700 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 167/180 F1 Macro Score: 0.94098 - 8s - loss: 0.0802 - acc: 0.9701 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 168/180 F1 Macro Score: 0.94100 - 8s - loss: 0.0798 - acc: 0.9702 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 169/180 F1 Macro Score: 0.94099 - 8s - loss: 0.0801 - acc: 0.9702 - val_loss: 0.0794 - val_acc: 0.9697 Epoch 170/180 F1 Macro Score: 0.94103 - 8s - loss: 0.0800 - acc: 0.9701 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 171/180 F1 Macro Score: 0.94086 - 8s - loss: 0.0813 - acc: 0.9699 - val_loss: 0.0796 - val_acc: 0.9696 Epoch 172/180 F1 Macro Score: 0.94096 - 8s - loss: 0.0803 - acc: 0.9701 - val_loss: 0.0795 - val_acc: 0.9696 Epoch 173/180 F1 Macro Score: 0.94101 - 8s - loss: 0.0810 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 174/180 F1 Macro Score: 0.94096 - 8s - loss: 0.0803 - acc: 0.9701 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 175/180 F1 Macro Score: 0.94097 - 8s - loss: 0.0799 - acc: 0.9702 - val_loss: 0.0796 - val_acc: 0.9697 Epoch 176/180 F1 Macro Score: 0.94099 - 8s - loss: 0.0802 - acc: 0.9702 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 177/180 F1 Macro Score: 0.94098 - 8s - loss: 0.0801 - acc: 0.9702 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 178/180 F1 Macro Score: 0.94092 - 8s - loss: 0.0801 - acc: 0.9701 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 179/180 F1 Macro Score: 0.94101 - 8s - loss: 0.0801 - acc: 0.9702 - val_loss: 0.0795 - val_acc: 0.9697 Epoch 180/180 F1 Macro Score: 0.94095 - 8s - loss: 0.0799 - acc: 0.9701 - val_loss: 0.0794 - val_acc: 0.9697 Training fold 5 completed. macro f1 score : 0.94095 Training completed. oof macro f1 score : 0.94030 save path: ./../data/output/submission_nb035_cv_0.9403.csv Training completed... CPU times: user 1h 41min 24s, sys: 11min 25s, total: 1h 52min 49s Wall time: 1h 57min 57s	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
analysis	df_tr = pd.read_csv(PATH_TRAIN) batch_list = [] for n in range(10): batchs = np.ones(500000)*n batch_list.append(batchs.astype(int)) batch_list = np.hstack(batch_list) df_tr['batch'] = batch_list # group 特徴量を作成 group = group_feat_train(df_tr) df_tr = pd.concat([df_tr, group], axis=1) y = df_tr['open_channels'].values oof = np.argmax(oof_, axis=1).astype(int) for group in sorted(df_tr['group'].unique()): idxs = df_tr['group'] == group oof_grp = oof[idxs].astype(int) y_grp = y[idxs] print(f'group_score({group}): {f1_macro(y_grp, oof_grp):4f}')	group_score(0): 0.332464 group_score(1): 0.779841 group_score(2): 0.973168 group_score(3): 0.997029 group_score(4): 0.847571	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
可視化	x_idx = np.arange(len(df_tr)) idxs = y != oof failed = np.zeros(len(df_tr)) failed[idxs] = 1 n = 200 b = np.ones(n)/n failed_move = np.convolve(failed, b, mode='same') fig, axs = plt.subplots(2, 1, figsize=(20, 6)) axs = axs.ravel() # fig = plt.figure(figsize=(20, 3)) for i_gr, group in enumerate(sorted(df_tr['group'].unique())): idxs = df_tr['group'] == group axs[0].plot(np.arange(len(df_tr))[idxs], df_tr['signal'].values[idxs], color=cp[i_gr], label=f'group={group}') for x in range(10): axs[0].axvline(x500000 + 500000, color='gray') axs[0].text(x500000 + 250000, 0.6, x) axs[0].plot(x_idx, failed_move, '.', color='black', label='failed_mv') axs[0].set_xlim(0, 5500000) axs[0].legend() axs[1].plot(x_idx, y) axs[1].set_xlim(0, 5500000) # fig.legend()	_____no_output_____	MIT	nb/035_submission.ipynb	fkubota/kaggle-University-of-Liverpool-Ion-Switching
Using Interrupts and asyncio for Buttons and SwitchesThis notebook provides a simple example for using asyncio I/O to interact asynchronously with multiple input devices. A task is created for each input device and coroutines used to process the results. To demonstrate, we recreate the flashing LEDs example in the getting started notebook but using interrupts to avoid polling the GPIO devices. The aim is have holding a button result in the corresponding LED flashing. Initialising the EnvironmentFirst we import an instantiate all required classes to interact with the buttons, switches and LED and ensure the base overlay is loaded.	from pynq import PL from pynq.overlays.base import BaseOverlay base = BaseOverlay("base.bit")	_____no_output_____	BSD-3-Clause	Pynq-ZU/base/notebooks/board/asyncio_buttons.ipynb	Xilinx/PYNQ-ZU
Define the flash LED taskNext step is to create a task that waits for the button to be pressed and flash the LED until the button is released. The `while True` loop ensures that the coroutine keeps running until cancelled so that multiple presses of the same button can be handled.	import asyncio async def flash_led(num): while True: await base.buttons[num].wait_for_value_async(1) while base.buttons[num].read(): base.leds[num].toggle() await asyncio.sleep(0.1) base.leds[num].off()	_____no_output_____	BSD-3-Clause	Pynq-ZU/base/notebooks/board/asyncio_buttons.ipynb	Xilinx/PYNQ-ZU
Create the taskAs there are four buttons we want to check, we create four tasks. The function `asyncio.ensure_future` is used to convert the coroutine to a task and schedule it in the event loop. The tasks are stored in an array so they can be referred to later when we want to cancel them.	tasks = [asyncio.ensure_future(flash_led(i)) for i in range(4)]	_____no_output_____	BSD-3-Clause	Pynq-ZU/base/notebooks/board/asyncio_buttons.ipynb	Xilinx/PYNQ-ZU
Monitoring the CPU UsageOne of the advantages of interrupt-based I/O is to minimised CPU usage while waiting for events. To see how CPU usages is impacted by the flashing LED tasks we create another task that prints out the current CPU utilisation every 3 seconds.	import psutil async def print_cpu_usage(): # Calculate the CPU utilisation by the amount of idle time # each CPU has had in three second intervals last_idle = [c.idle for c in psutil.cpu_times(percpu=True)] while True: await asyncio.sleep(3) next_idle = [c.idle for c in psutil.cpu_times(percpu=True)] usage = [(1-(c2-c1)/3) * 100 for c1,c2 in zip(last_idle, next_idle)] print("CPU Usage: {0:3.2f}%, {1:3.2f}%".format(*usage)) last_idle = next_idle tasks.append(asyncio.ensure_future(print_cpu_usage()))	_____no_output_____	BSD-3-Clause	Pynq-ZU/base/notebooks/board/asyncio_buttons.ipynb	Xilinx/PYNQ-ZU
Run the event loopAll of the blocking wait_for commands will run the event loop until the condition is met. All that is needed is to call the blocking `wait_for_level` method on the switch we are using as the termination condition. While waiting for switch 0 to get high, users can press any push button on the board to flash the corresponding LED. While this loop is running, try opening a terminal and running `top` to see that python is consuming no CPU cycles while waiting for peripherals. As this code runs until the switch 0 is high, make sure it is low before running the example.	if base.switches[0].read(): print("Please set switch 0 low before running") else: base.switches[0].wait_for_value(1)	CPU Usage: 0.67%, 11.67% CPU Usage: 0.00%, 0.33% CPU Usage: 0.00%, 0.33% CPU Usage: 0.00%, 0.33% CPU Usage: 0.00%, 0.33% CPU Usage: 0.00%, 0.33%	BSD-3-Clause	Pynq-ZU/base/notebooks/board/asyncio_buttons.ipynb	Xilinx/PYNQ-ZU
Clean upEven though the event loop has stopped running, the tasks are still active and will run again when the event loop is next used. To avoid this, the tasks should be cancelled when they are no longer needed.	[t.cancel() for t in tasks]	_____no_output_____	BSD-3-Clause	Pynq-ZU/base/notebooks/board/asyncio_buttons.ipynb	Xilinx/PYNQ-ZU
Now if we re-run the event loop, nothing will happen when we press the buttons. The process will block until the switch is set back down to the low position.	base.switches[0].wait_for_value(0)	_____no_output_____	BSD-3-Clause	Pynq-ZU/base/notebooks/board/asyncio_buttons.ipynb	Xilinx/PYNQ-ZU
Making Simple Plots Objectives+ Learn how to make a simple 1D plot in Python.+ Learn how to find the maximum/minimum of a function in Python.We will use [Problem 4.B.2](https://youtu.be/w-IGNU2i3F8) of the lecturebook as a motivating example.We find that the moment of the force $\vec{F}$ about point A is:$$\vec{M_A} = (bF\cos\theta - dF\sin\theta)\hat{k}.$$Let's plot the component of the moment as a function of $\theta$.For this, we will use the Python module [matplotlib](https://matplotlib.org).	import numpy as np # for numerical algebra import matplotlib.pyplot as plt # this is where the plotting capabilities are # The following line is need so that the plots are embedded in the Jupyter notebook (remove when not using Jupyter) %matplotlib inline # Define a function that computes the moment magnitude as a function of all other parameters def M_A(theta, b, d, F): """ Compute the k component of the moment of F about point A given all the problem parameters. """ return b * F * np.cos(theta) - d * F * np.sin(theta) # Choose some parameters b = 0.5 # In meters d = 2. # In meters F = 2. # In kN # The thetas on which we will evaluate the moment for plotting thetas = np.linspace(0, 2 * np.pi, 100) # The moment on these thetas: M_As = M_A(thetas, b, d, F) # Let's plot plt.plot(thetas / (2. * np.pi) * 360, M_As, lw=2) plt.xlabel(r'$\theta$ (degrees)') plt.ylabel('$M_A$ (kN)');	_____no_output_____	MIT	making_simple_plots.ipynb	PurdueMechanicalEngineering/me270
Now, let's put two lines in the same plot.Let's compare the moments when we change $d$ from 2 meters to 3.5 meters.	# We already have M_A for d=2 m (and all other paramters to whichever values we gave them) # Let's copy it: M_As_case_1 = M_As # And let's compute it again for d=3.5 m d = 3.5 # In m M_As_case_2 = M_A(thetas, b, d, F) # Let's plot both of them in the same figure plt.plot(thetas / (2. * np.pi) * 360, M_As_case_1, lw=2, label='Case 1') plt.plot(thetas / (2. * np.pi) * 360, M_As_case_2, '--', lw=2, label='Case 2') plt.xlabel(r'$\theta$ (degrees)') plt.ylabel('$M_A$ (kN)') plt.legend(loc='best')	_____no_output_____	MIT	making_simple_plots.ipynb	PurdueMechanicalEngineering/me270
Finally, let's see how we can make interactive plots.We will use the Python module [ipywidgets](https://ipywidgets.readthedocs.io/en/stable/) and in particular the function [ipywidgets.interact](https://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html).	from ipywidgets import interact # Loading the module # Interact needs a function that does the plotting given the parameters. # Let's make it: def make_plots(b=0.5, d=3., F=1.): # X=val defines default values for the function """ Make the plot. """ thetas = np.linspace(0, 2. * np.pi, 100) M_As = M_A(thetas, b, d, F) plt.plot(thetas / (2. * np.pi) * 360, M_As, lw=2, label='Case 1') plt.ylim([-10., 10.]) plt.xlabel(r'$\theta$ (degrees)') plt.ylabel('$M_A$ (kN)')	_____no_output_____	MIT	making_simple_plots.ipynb	PurdueMechanicalEngineering/me270
Let's just check that the function works by calling it a few times:	# With no inputs it should use the default values make_plots() # You can specify all the inputs like this: make_plots(2., 3., 2.) # Or even by name (whatever is not specified gets the default value): make_plots(F=2.3)	_____no_output_____	MIT	making_simple_plots.ipynb	PurdueMechanicalEngineering/me270
Ok. Let's use interact now:	interact(make_plots, b=(0., 5., 0.1), # Range for b: (min, max, increment) d=(0., 5, 0.1), # Range for d F=(0., 2, 0.1) # Range for F );	_____no_output_____	MIT	making_simple_plots.ipynb	PurdueMechanicalEngineering/me270
VacationPy---- Note* Keep an eye on your API usage. Use https://developers.google.com/maps/reporting/gmp-reporting as reference for how to monitor your usage and billing.* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.	# Dependencies and Setup import matplotlib.pyplot as plt import pandas as pd import numpy as np import requests import gmaps import os # Import API key from api_keys import g_key	_____no_output_____	ADSL	VacationPy/VacationPy.ipynb	jgmoore10/python-api-challenge
Store Part I results into DataFrame* Load the csv exported in Part I to a DataFrame	city_data = pd.read_csv("../output_data/cities.csv") city_data.head()	_____no_output_____	ADSL	VacationPy/VacationPy.ipynb	jgmoore10/python-api-challenge
Humidity Heatmap* Configure gmaps.* Use the Lat and Lng as locations and Humidity as the weight.* Add Heatmap layer to map.	gmaps.configure(api_key=g_key) locations = city_data[["Lat", "Lng"]].astype(float) humidity = city_data["Humidity"].astype(float) fig = gmaps.figure() heat_layer = gmaps.heatmap_layer(locations, weights = humidity, dissipating = False, max_intensity = 100, point_radius = 1) fig.add_layer(heat_layer) fig	_____no_output_____	ADSL	VacationPy/VacationPy.ipynb	jgmoore10/python-api-challenge
Create new DataFrame fitting weather criteria* Narrow down the cities to fit weather conditions.* Drop any rows will null values.	narrowed_city_df = city_data.loc[(city_data["Humidity"]>=70) & (city_data["Wind Speed"]>=10) & \ (city_data["Cloudiness"] <= 20)].dropna() narrowed_city_df.head()	_____no_output_____	ADSL	VacationPy/VacationPy.ipynb	jgmoore10/python-api-challenge
Hotel Map* Store into variable named `hotel_df`.* Add a "Hotel Name" column to the DataFrame.* Set parameters to search for hotels with 5000 meters.* Hit the Google Places API for each city's coordinates.* Store the first Hotel result into the DataFrame.* Plot markers on top of the heatmap.	hotel_df = narrowed_city_df.reset_index(drop=True) hotel_df["Hotel Name"] = "" hotel_df # geocoordinates target_search = "Hotel" target_radius = 5000 target_type = "Hotels" params={ "radius":target_radius, "types":target_type, "keyword":target_search, "key":g_key } # NOTE: Do not change any of the code in this cell # Using the template add the hotel marks to the heatmap info_box_template = """ <dl> <dt>Name</dt><dd>{Hotel Name}</dd> <dt>City</dt><dd>{City}</dd> <dt>Country</dt><dd>{Country}</dd> </dl> """ # Store the DataFrame Row # NOTE: be sure to update with your DataFrame name hotel_info = [info_box_template.format(**row) for index, row in hotel_df.iterrows()] locations = hotel_df[["Lat", "Lng"]] # Add marker layer ontop of heat map markers = gmaps.marker_layer(locations) fig.add_layer(markers) # Display figure fig	_____no_output_____	ADSL	VacationPy/VacationPy.ipynb	jgmoore10/python-api-challenge
BEFORE YOU DO ANYTHING...In the terminal:1. Navigate to __inside__ your ILAS_Python repository.2. __COMMIT__ any un-commited work on your personal computer.3. __PULL__ any changes you have made using another computer.4. __PULL__ textbook updates (including homework answers). Control Flow Lesson GoalCompose simple programs to control the order in which the operators we have studied so far are executed. ObjectivesControl the flow of a program using:- __control statements__- __loops__ Why we are studying this:Control flow allows us to make __choices__ using our program.Control statements result in a decision being made as to which of __two or more possible paths__ to follow. Lesson structure: - Control Statements - `if` and `else`statements - `for` loops - `while` loops - `break` and `continue` statements - Review Exercises - Summary What is a __control statement__?Let's start with an example from the last seminar... Control StatementsIn the last seminar we looked at a simple computer program that returned Boolean (True or False) variables... Based on the current time of day, the program answers two questions:>__Is it lunchtime?__>`True`if it is lunch time.>__Is it time for work?__>`True`if it is `not`:- before work (`time < work_starts`)- after work (`time > work_ends `)- lunchtime (the previous question assigns the value `True` or `False` to variable `lunchtime`).	# Time-telling program time = 13.05 # current time work_starts = 8.00 # time work starts work_ends = 17.00 # time work ends lunch_starts = 13.00 # time lunch starts lunch_ends = 14.00 # time lunch ends # variable lunchtime is True if the time is between the start and end of lunchtime lunchtime = time >= lunch_starts and time < lunch_ends # variable work_time is True if the time is not... work_time = not ( time < work_starts # ... before work or time > work_ends # ... or after work or lunchtime) # ... or lunchtime print("Is it work time?") print(work_time) print("Is it lunchtime?") print(lunchtime)	Is it work time? False Is it lunchtime? True	MIT	3_Control_flow.ipynb	konshte/Python_K
What if we now want our computer program to do something based on these answers?To do this, we need to use control statements.Control statements allow us to make decisions in a program.This decision making is known as control flow. Control statements are a fundamental part of programming. Here is a control statement in pseudo code:This is an `if` statement. if A is true Perform task X For example if lunchtime is true Eat lunch We can check if an alternative to the `if` statement is true using an `else if` statement. if A is true Perform task X (only) else if B is true Perform task Y (only) Example: if lunchtime is true Eat lunch else if work_time is true Do work Often it is useful to include an `else` statement.If none of the `if` and `else if` statements are satisfied, the code following the `else` statement will be executed. if A is true Perform task X (only) else if B is true Perform task Y (only) else Perform task Z (only) if lunchtime is true Eat lunch else if work_time is true Do work else Go home Let's get a better understanding of control flow statements by completing some examples. `if` and `else` statementsLet's consider a simple example that demonstrates a Python if-else control statement. It uses the lunch/work example from the previous seminar.__Note:__ In Python, "else if" is written: `elif`	# Time-telling program time = 13.05 # current time work_starts = 8.00 # time work starts work_ends = 17.00 # time work ends lunch_starts = 13.00 # time lunch starts lunch_ends = 14.00 # time lunch ends # variable lunchtime is True if the time is between the start and end of lunchtime lunchtime = time >= lunch_starts and time < lunch_ends # variable work_time is True if the time is not... work_time = not ( time < work_starts # ... before work or time > work_ends # ... or after work or lunchtime) # ... or lunchtime #print("Is it work time?") #print(work_time) #print("Is it lunchtime?") #print(lunchtime) if lunchtime: # if lunchtime == True: print("Eat lunch") elif work_time: # elif work_time == True: print("Do work") else: print("Go home")	Eat lunch	MIT	3_Control_flow.ipynb	konshte/Python_K
__Remember:__ The program assigns the variables lunchtime and work_time the values `True` or `False`.Therefore when we type: `if lunchtime`the meaning is the same as: `if lunchtime == True` Here is another example, using algebraic operators to modify the value of an initial variable, `x`. The modification of `x` and the message printed depend on the initial value of `x`.	#The input to the program is variable `x`. x = -10.0 # Initial x value if x > 0.0: print('Initial x is greater than zero') #The program prints a message... x -= 20.0 # ...and modifies `x`. elif x < 0.0: print('Initial x is less than zero') x += 21.0 else: print('Initial x is not less than zero and not greater than zero, therefore it must be zero') x *= 2.5 print("Modified x = ", x)	Initial x is less than zero Modified x = 11.0	MIT	3_Control_flow.ipynb	konshte/Python_K
__Note:__ The program uses the short-cut algebraic operators that you learnt to use in the last seminar. __Try it yourself__In the cell code cell above, try:- changing the operations performed on `x`- changing the value of `x` a few times.Re-run the cell to see the different paths the program can follow. Look carefully at the structure of the `if`, `elif`, `else`, control statement:__The control statement begins with an `if`__, followed by the expression to check. At the end of the `if` statement you must put a colon (`:`) ````pythonif x > 0.0: ```` After the `if` statement, indent the code to be run in the case that the `if` statement is `True`. To end the code to be run, simply stop indenting: ````pythonif x > 0.0: print('Initial x is greater than zero') x -= 20.0```` The indent can be any number of spaces.The number of spaces must be the same for all lines of code to be run if the `if` statement is True.Jupyter Notebooks automatically indent 4 spaces.This is considered best practise. `if x > 0.0` is: - `True`: - The indented code is executed. - The control block is exited. - The program moves past any subsequent `elif` or `else` statements. - `False`: the program moves past the inented code to the next (non-indented) part of the program... In this the next (non-indented) part of the program is `elif` (else if).The elif statement is evaluated.(Notice that the code is structured in the same way as the `if` statement.):```pythonif x > 0.0: print('Initial x is greater than zero') x -= 20.0 elif x < 0.0: print('Initial x is less than zero') x += 21.0``` `elif x < 0.0`:- `True`: - The indented code is executed. - The control block is exited. - The program moves past any subsequent `elif` or `else` statements. - `False`: the program moves past the indented code to the next (non-indented) part of the program. If none of the preceding `if` or `elif` stements are true. e.g. in this example: - `x > 0.0` is `False` - `x < 0.0` is `False`the code following the `else` statement is executed.```pythonif x > 0.0: print('Initial x is greater than zero') x -= 20.0elif x < 0.0: print('Initial x is less than zero') x += 21.0else: print('Initial x is not less than zero and not greater than zero, therefore it must be zero')``` Evaluating data against different criteria is extremely useful for solving real-world mathematical problems. Let's look at a simple example... Real-World Example: currency tradingTo make a comission (profit), a currency trader sells US dollars to travellers above the market rate. The multiplier used to calculate the amount recieved by customer is shown in the table:\|Amount (JPY) \|Multiplier \|\|--------------------------------------------\|-------------------------\|\| Less than $100$ \| 0.9 \| \| From $100$ and less than $1,000$ \| 0.925 \| \| From $1,000$ and less than $10,000$ \| 0.95 \| \| From $10,000$ and less than $100,000$ \| 0.97 \| \| Over $100,000$ \| 0.98 \| The currency trader charges more if the customer pays with cash. If the customer pays with cash, the currency trader reduces the rate by an __additional__ 10% after conversion. (If the transaction is made electronically, they do not). __Current market rate:__ 1 JPY = 0.0091 USD.__Effective rate:__ The rate that the customer receives based on the amount in JPY to be changed. The program calculates the __effective rate__ using: - The reduction based on the values in the table. - An additional 10% reduction (mutiplier = 0.9) if the transaction is made in cash.	JPY = 1_000_000 # The amount in JPY to be changed into USD cash = False # True if transaction is in cash, otherwise False market_rate = 0.0091 # 1 JPY is worth this many dollars at the market rate # Apply the appropriate reduction depending on the amount being sold if JPY < 10_000: multiplier = 0.9 elif JPY < 100_000: multiplier = 0.925 * market_rate * JPY elif JPY < 1_000_000: multiplier = 0.95 * market_rate * JPY elif JPY < 10_000_000: multiplier = 0.97 * market_rate * JPY else: # JPY > 10,000,000 multiplier = 0.98 * market_rate * JPY # Apply the appropriate reduction depending if the transaction is made in cash or not if cash: cash_multiplier = 0.9 else: cash_multiplier = 1 # Calculate the total amount sold to the customer USD = JPY * market_rate * multiplier * cash_multiplier print("Amount in JPY sold:", JPY) print("Amount in USD purchased:", USD) print("Effective rate:", USD/JPY)	Amount in JPY sold: 1000000 Amount in USD purchased: 80325700.0 Effective rate: 80.3257	MIT	3_Control_flow.ipynb	konshte/Python_K
__Note:__ - We can use multiple `elif` statements within a control block. - We can use multipe `if` statements. When the program executes and exits a control block, it moves to the next `if` statement. - __Readability:__ Underscores _ are placed between 0s in long numbers to make them easier to read. You DO NOT need to include underscores for Python to interpret the number correctly. You can place the underscores wherever you like in the sequence of digits that make up the number. __Try it yourself__In your textbook, try changing the values of `JPY` and `cash` a few times.Re-run the cell to see the different paths the program can follow. `for` loopsLoops are used to execute a command repeatedly.A loop is a block that repeats an operation a specified number of times (loops). To learn about loops we are going to use the function `range()`. `range`The function `range` gives us a sequence of integer numbers.`range(3, 6)` returns integer values starting from 3 and ending at 6.i.e.> 3, 4, 5Note this does not include 6. We can change the starting value. For example for integer values starting at 0 and ending at 4: `range(0,4)`returns:> 0, 1, 2, 3`range(4)` is a __shortcut__ for range(0, 4) range (0,5)range (5,9) Simple `for` loops The statement ```pythonfor i in range(0, 5):```says that we want to run the indented code five times.	for i in range(0, 6): print(i)	0 1 2 3 4 5	MIT	3_Control_flow.ipynb	konshte/Python_K
The first time through, the value of i is equal to 0.The second time through, its value is 1.Each loop the value `i` increases by 1 (0, 1, 2, 3, 4) until the last time when its value is 4. Look carefully at the structure of the `for` loop: - `for` is followed by the condition being checked. - : colon at the end of the `for` statement. - The indented code that follows is run each time the code loops. (The __same of spaces__ should be used for all indents) - To end the `for` loop, simply stop indenting.	for i in range(-2, 3): print(i) print('The end of the loop')	-2 -1 0 1 2 The end of the loop	MIT	3_Control_flow.ipynb	konshte/Python_K
The above loop starts from -2 and executes the indented code for each value of i in the range (-2, -1, 0, 1, 2).When the loop has executed the code for the final value `i = 2`, it moves on to the next unindented line of code.	for n in range(4): print("----") print(n, n**2)	---- 0 0 ---- 1 1 ---- 2 4 ---- 3 9	MIT	3_Control_flow.ipynb	konshte/Python_K
The above executes 4 loops.The statement ```pythonfor n in range(4):```says that we want to loop over four integers, starting from 0. Each loop the value `n` increases by 1 (0, 1, 2 3). __Try it yourself__Go back and change the __range__ of input values in the last three cells and observe the change in output. If we want to step by three rather than one:	for n in range(0, 10, 3): print(n)	0 3 6 9	MIT	3_Control_flow.ipynb	konshte/Python_K
If we want to step backwards rather than forwards we __must__ include the step size:	for n in range(10, 0, -1): print(n)	10 9 8 7 6 5 4 3 2 1	MIT	3_Control_flow.ipynb	konshte/Python_K
For example...	for n in range(10, 0): print(n)	_____no_output_____	MIT	3_Control_flow.ipynb	konshte/Python_K
...does not return any values because there are no values that lie between 10 and 0 when counting in the positive direction from 10. __Try it yourself.__In the cell below write a `for` loop that:- starts at `n = 9`- ends at `n = 3` (and includes `n = 3`)- loops __backwards__ through the range in steps of -3 - prints `n`$^2$ at each loop.	# For loop for n in range(9, 2, -3): print ("-----") print(n, n**2)	----- 9 81 ----- 6 36 ----- 3 9	MIT	3_Control_flow.ipynb	konshte/Python_K
For loops are useful for performing operations on large data sets.We often encounter large data sets in real-world mathematical problems. A simple example of this is converting multiple values using the same mathematical equation to create a look-up table... Real-world Example: conversion table from degrees Fahrenheit to degrees CelsiusWe can use a `for` loop to create a conversion table from degrees Fahrenheit ($T_F$) to degrees Celsius ($T_c$).Conversion formula:$$T_c = 5(T_f - 32)/9$$Computing the conversion from -100 F to 200 F in steps of 20 F (not including 200 F):	print("T_f, T_c") for Tf in range(-100, 200, 20): print(Tf, "\t", round(((Tf - 32) * 5 / 9), 3))	T_f, T_c -100 -73.333 -80 -62.222 -60 -51.111 -40 -40.0 -20 -28.889 0 -17.778 20 -6.667 40 4.444 60 15.556 80 26.667 100 37.778 120 48.889 140 60.0 160 71.111 180 82.222	MIT	3_Control_flow.ipynb	konshte/Python_K
`while` loopsA __`for`__ loop performs an operation a specified number of times. ```python for x in range(5): print(x)``` A __`while`__ loop performs a task while a specified statement is true. ```pythonx = 0while x < 5: print(x)``` The structure of a `while` loop is similar to a `for` loop.- `while` is followed by the condition being checked.- : colon at the end of the `while` statement. - The indented code that follows is repeatedly executed until the `while` statement (e.g. `x It can be quite easy to crash your computer using a `while` loop. e.g. if we don't modify the value of x each time the code loops:```pythonx = 0while x < 5: print(x) x += 1 ```will continue indefinitely since `x < 5 == False` will never be satisfied.This is called an infinite loop. To perform the same function as the `for` loop we need to increment the value of `x` within the loop:	x = 0 print("Start of while statement") while x < 5: print(x) x += 1 # Increment x print("End of while statement")	Start of while statement 0 1 2 3 4 End of while statement	MIT	3_Control_flow.ipynb	konshte/Python_K
`for` loops are often safer when performing an operation on a set range of values.	x = -2 print("Start of for statement") for y in range(x,5): print(y) print("End of for statement")	Start of for statement -2 -1 0 1 2 3 4 End of for statement	MIT	3_Control_flow.ipynb	konshte/Python_K
Here is another example of a `while` loop.	x = 0.9 while x > 0.001: # Square x (shortcut x = x) x = x x print(round(x, 6))	0.81 0.6561 0.430467 0.185302 0.034337 0.001179 1e-06	MIT	3_Control_flow.ipynb	konshte/Python_K
If we use an initial value of $x \ge 1$, an infinite loop will be generted.`x` will increase with each loop, meaning `x` will always be greater than 0.001.e.g. ```pythonx = 2while x > 0.001: x = x * x print(x)``` However, using a `for` loop is a less appropriate solution in this case.We may not know beforehand how many steps are required before `x > 0.001` becomes false. To avoid errors, it is good practice to check that $x < 1$ before entering the `while` loop e.g.	x = 0.9 if x < 1: while x > 0.001: # Square x (shortcut x = x) x = x x print(round(x, 6)) else: print("x is greater than one, infinite loop avoided")	0.81 0.6561 0.430467 0.185302 0.034337 0.001179 1e-06	MIT	3_Control_flow.ipynb	konshte/Python_K
__Try it for yourself:__In the cell above change the value of x to above or below 1.Observe the output. __Try it for yourself:__In the cell below: - Create a variable,`x`, with the initial value 50 - Each loop: 1. print x 1. reduce the value of x by half - Exit the loop when `x` < 3	# While loop	_____no_output_____	MIT	3_Control_flow.ipynb	konshte/Python_K
`break` and `continue`. `break`Sometimes we want to break out of a `for` or `while` loop. For example in a `for` loop we can check if something is true, and then exit the loop prematurely, e.g	for x in range(10): print(x) if x == 5: print("Time to break out") break	0 1 2 3 4 5 Time to break out	MIT	3_Control_flow.ipynb	konshte/Python_K
Let's look at how we can use this in a program... The following program __finds prime numbers__.__Prime number:__ A positive integer, greater than 1, that has no positive divisors other than 1 and itself (2, 3, 5, 11, 13, 17....)The program checks (integer) numbers, `n` up to a limit `N` and prints the prime numbers. We can determine in `n` is a prime nunber by diving it by every number in the range 2 to `n`.If any of these calculations has a remainder equal to zero, n is not a prime number.	N = 50 # Check numbers up 50 for primes (excludes 50) # Loop over all numbers from 2 to 50 (excluding 50) for n in range(2, N): # Assume that n is prime n_is_prime = True # Check if n divided by (any number in the range 2 to n) returns a remainder equal to 0 for m in range(2, n): # If the remainder is zero, n is not a prime number if n % m == 0: n_is_prime = False # If n is prime, print to screen if n_is_prime: print(n)	2 3 5 7 11 13 17 19 23 29 31 37 41 43 47	MIT	3_Control_flow.ipynb	konshte/Python_K
Notice that our program contains a second `for` loop. For each value of n, it loops through incrementing values of m in the range (2 to n):```python Check if n can be divided by m m ranges from 2 to n (excluding n)for m in range(2, n):```before incrementing to the next value of n.We call this a nested loop.The indents in the code show where loops are nested. Here it is again without the comments:	N = 50 # for loop 1 for n in range(2, N): n_is_prime = True # for loop 2 for m in range(2, n): if n % m == 0: n_is_prime = False if n_is_prime: print(n)	2 3 5 7 11 13 17 19 23 29 31 37 41 43 47	MIT	3_Control_flow.ipynb	konshte/Python_K
As n gets larger, dividing it by every number in the range (2, n) becomes more and more inefficient. A `break` statement allows us to exit the loop as soon as a remainder equal to zero is returned (indicating that n is not a prime number). In the program below, a break statement is added.As soon as a number is found to be not prime, the program breaks out of loop 2 and goes to the next value of n in loop 1.By placing `else` one level up from `if` the program will iterate through all values of m before printing n if n is prime.	N = 55 # for loop 1 for n in range(2, N): # for loop 2 for m in range(2, n): if n % m == 0: break else: # if n is prime print(n)	3 5 5 5 7 7 7 7 7 9 11 11 11 11 11 11 11 11 11 13 13 13 13 13 13 13 13 13 13 13 15 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 21 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 25 25 25 27 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 33 35 35 35 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 39 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 45 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 49 49 49 49 49	MIT	3_Control_flow.ipynb	konshte/Python_K
`continue`Sometimes, instead of stopping the loop we want to go to the next iteration in a loop, skipping the remaining code.For this we use `continue`. The example below loops over 20 numbers (0 to 19) and checks if the number is divisible by 4. If the number is not divisible by 4:- it prints a message - it moves to the next value. If the number is divisible by 4 it continues to the next value in the loop, without printing.	for j in range(1, 20): if j % 4 == 0: # Check remainer of j/4 continue # continue to next value of j print(j, "is not a multiple of 4")	1 is not a multiple of 4 2 is not a multiple of 4 3 is not a multiple of 4 5 is not a multiple of 4 6 is not a multiple of 4 7 is not a multiple of 4 9 is not a multiple of 4 10 is not a multiple of 4 11 is not a multiple of 4 13 is not a multiple of 4 14 is not a multiple of 4 15 is not a multiple of 4 17 is not a multiple of 4 18 is not a multiple of 4 19 is not a multiple of 4	MIT	3_Control_flow.ipynb	konshte/Python_K
To compare, if we used `break` instead of `continue`:	for j in range(1, 20): if j % 4 == 0: # Check remainer of j/4 break # continue to next value of j print(j, "is not a multiple of 4")	1 is not a multiple of 4 2 is not a multiple of 4 3 is not a multiple of 4	MIT	3_Control_flow.ipynb	konshte/Python_K
__Try it yourself__We can use a `for` loop to perform an operation on each character of a string.```Pythonstring = "string"for i in range(len(sting)): print(sting[i])``` In the cell below, loop through the characters of the string.Use `continue` to only print the letters of the word sting.	# Print the letters of the word sting string = "string"	_____no_output_____	MIT	3_Control_flow.ipynb	konshte/Python_K
Review ExercisesHere are a series of engineering problems for you to practise each of the new Python skills that you have learnt today. Review Exercise: `while` loops.In the cell below, write a while loop that with each loop:- prints the value of `x`- then decreases the value of x by 0.5as long as `x` remains positive.Jump to While Loops	x = 4 while x > 0: print(x) x -= 0.5 # Example Solution while (x > 0): print(x) x -= 0.5	_____no_output_____	MIT	3_Control_flow.ipynb	konshte/Python_K
Review Exercise: `for` loopsIn the cell below, write a `for` loop to print the even numbers from 2 to 100, inclusive.	# for loop to print the even numbers from 2 to 20, inclusive. for n in range (2, 21): print (n) # Example Solution for i in range(2, 21, 2): print(i)	_____no_output_____	MIT	3_Control_flow.ipynb	konshte/Python_K
Review Excercise: `for` loops and `if` statementsIn the cell below, write a for loop to alternately print `Red` then `Blue` 3 times. i.e. Red Blue Red Blue Red Blue	# Alternately print Red and Blue for n in range (1, 7): if n % 2 == 0: print("red") elif # Example Solution colour = "Red" for n in range(6): print(colour) if colour == "Red": colour = "Blue" else: colour = "Red"	_____no_output_____	MIT	3_Control_flow.ipynb	konshte/Python_K
Review Exercise: `continue`In the cell below, loop through the characters of the string.Use `continue` to only print the letters of the word sing.Hint: Refer to __Logical Operators__ (Seminar 2). Jump to continue	# Print the letters of the word sing string = "string" # Example Solution string = "string" for i in range(len(string)): if string[i] == "r" or string[i] == "t": continue print(string[i])	_____no_output_____	MIT	3_Control_flow.ipynb	konshte/Python_K
Review Exercise: `for` loops and `if`, `else` and `continue` statements.__(A)__ In the cell below, use a for loop to print the square roots of the first 25 odd positive integers. (Remember, the square root of a number, $x$ can be found by $x^{1/2}$)__(B)__ If the number generated is greater than 3 and smaller than 5, print "`skip`" and __`continue`__ to the next iteration without printing the number.Hint: Refer to __Logical Operators__ (Seminar 2). Jump to for loopsJump to if and else statementsJump to continue	# square roots of the first 25 odd positive integers # Example Solution for x in range(1, 50, 2): if((x (1/2) > 3) and (x (1/2) < 5)): print("skip") continue print(x ** (1/2))	_____no_output_____	MIT	3_Control_flow.ipynb	konshte/Python_K
Prove that for integers $a,\;b,\;\dots$(1) $(a, b) = 1, \; c \| a, \; d \| a \implies (c, d) = 1$Suppose $(c, d) = e > 1$. Then $e \| c$ and $c \| a$ implies $e \| a$; similarly $e \| b$ so $(a, b) > 1$, a contradiction, and therefore $(c, d) = 1$. $\;\;\;\boxdot$(2) $(a, b) = (a, c) = 1 \implies (a, bc) = 1$(3) $(a, b) = 1 \implies (a^n, b^k) = 1 \; \; \forall \; \; n \ge 1, k \ge 1$(4) $(a, b) = 1 \implies (a + b, a - b) = 1 \; or \; 2$(5) $(a, b) = 1 \implies (a + b, a^2 - ab + b^2) = 1 \; or \; 3$(6) $(a, b) = 1, \; d\|(a + b) \implies (a, d) = (b, d) = 1$ (7) A rational number $a/b$ with $(a, b) = 1$ is a reduced fraction. If the sum of two reduced fractions is an integer, say $(a/b) + (c/d) = n$, prove that $\|b\| = \|d\|$.(8) An integer is called squarefree if it is not divisible by the square of any prime. Prove that for every $n \ge 1$ there exist uniquely determined $a > 0$ and $b > 0$ such that $n=a^2b$, where $b$ is squarefree. ...(11) Prove that $n^4 + 4$ is composite if $n > 1$. *Solution*I first tried cases for the ones-digit. For example $n$ even gives $n^4 + 4$ also even and $n$ ending in 1, 3, 7 or 9 gives $n^4 + 4$ ending in 5. However (particularly because the last case does not resolve in this manner) the right thing to try is factoring $n^4 + 4$ in some obvious way: Constants 1 and 4 or 2 and 2. $n^4 + 4 = (n^2 + a \cdot n + 2) (n^2 + b \cdot n + 2)$This gives $n^4 + b \cdot n^3 + 2 n^2 + a \cdot n^3 + a \cdot b \cdot n^2 + 2 \cdot a \cdot n + 2 n^2 + 2 \cdot b \cdot n + 4$$n^4 + 4$ plus stuff that needs to be zero: $(b + a)\cdot n^3 + (4 + a \cdot b)\cdot n^2 + (2 \cdot (a + b))\cdot n$This means $a = -b$ and $a \cdot b = -4$. Great: $a = 2$ and $b = -2$. $n^4 + 4 = (n^2 + 2n + 2)(n^2 - 2n + 2)\;\;\;\;\boxdot$	def pf(n): pfn, i = [], 2 while i * i < n: while n%i == 0: pfn.append(i); n = n / i i = i + 1 pfn.append(int(n)) return pfn def npf(n): return len(pf(n)) def isprime(n): if npf(n) == 1: return True return False for a in range(3): s = a * 10 + 5 t = sss*s + 4 if isprime(t): print(str(t) + ' is prime') else: print(str(t) + ' factors are ' + str(pf(t)))	629 factors are [17, 37] 50629 factors are [197, 257] 390629 factors are [577, 677]	MIT	T1_chapter1_exercises.ipynb	robfatland/boojum
... ...(20) Let $d = (826, 1890)$. Use the Euclidean algorithm to compute $d$, then express $d$ as a linear combination of 826 and 1890Solution$1890 = 826 \cdot 2 + 238$$826 = 238 \cdot 3 + 112$$238 = 112 \cdot 2 + 14$$112 = 14 \cdot 8 + 0$$d = 14$$d = u \cdot 826 + v \cdot 1890$ or equivalently $1 = u \cdot 59 + v \cdot 135$Taking $u$ positive it can take on values ${ 4, 9, 14, 19, \dots }$.--a miracle occurs--$(d = 14) = 254 \cdot 826 - 111 \cdot 1890$	254826-1111890	_____no_output_____	MIT	T1_chapter1_exercises.ipynb	robfatland/boojum
Table of Contents1  Plot Validation and Train loss2  Extract relevant Data to df2.1  Get best result2.2  Consider Outliers3  Results by model3.1  Remove Duplicates4  Each variable plotted against loss:5  Investigate "band" in loss-model plot5.1  Extract the different bands and inpsect6  Investigate Duplicates7  Investigate Best	%load_ext autoreload %autoreload 2 %cd .. import os import sys from notebooks import utils from matplotlib import pyplot as plt %matplotlib inline import seaborn as sns sns.set() #import pipeline # parent_dir = os.path.abspath(os.path.join(os.getcwd(), os.pardir)) # sys.path.append(parent_dir) #to import pipeline %ls experiments ###CHANGE THIS FILE TO THE SUBDIRECTORY OF INTEREST: #exp_dirs = ["experiments/07b/", "experiments/DA3_2/07a/0", "experiments/DA3_2/07a/1"] exp_dirs = ["experiments/retrain/"] results = utils.extract_res_from_files(exp_dirs) #load data when utils isnt working: if False: import pickle res_fp = "experiments/results/ResNeXt/res.txt" with open(res_fp, "rb") as f: results = pickle.load(f)	_____no_output_____	MIT	notebooks/.ipynb_checkpoints/CAE_zoo_analysis-checkpoint.ipynb	scheng1992/Data_Assimilation
Plot Validation and Train loss	ylim = (0, 3000) ylim2 = (70,100) utils.plot_results_loss_epochs(results, ylim1=ylim, ylim2=ylim2)	(2, 3)	MIT	notebooks/.ipynb_checkpoints/CAE_zoo_analysis-checkpoint.ipynb	scheng1992/Data_Assimilation
Extract relevant Data to dfUse minimum validation loss as criterion. In theory (if we had it) it would be better to use DA MAE	df_res = utils.create_res_df(results) df_res_original = df_res.copy() #save original (in case you substitute out) df_res	_____no_output_____	MIT	notebooks/.ipynb_checkpoints/CAE_zoo_analysis-checkpoint.ipynb	scheng1992/Data_Assimilation
Get best result	df_res["valid_loss"].idxmin() print(df_res.loc[df_res["valid_loss"].idxmin()]) df_res.loc[df_res["valid_loss"].idxmin()]["path"]	model CLIC valid_loss 397.938 activation prelu latent_dims ?? num_layers ?? total_channels None channels/layer ?? augmentation 1 batch_norm 0 channels see model def conv_changeover 10 dropout 0 first_channel e learning_rate 0.0002 path experiments/DA3_2/07a/0 Name: 4, dtype: object	MIT	notebooks/.ipynb_checkpoints/CAE_zoo_analysis-checkpoint.ipynb	scheng1992/Data_Assimilation
Consider Outliers	#consider third experiment run (lots of outliers) df3 = df_res[df_res["path"].str.contains("CAE_zoo3")] df_outlier = df_res[df_res["valid_loss"] > 150000] df_outlier	_____no_output_____	MIT	notebooks/.ipynb_checkpoints/CAE_zoo_analysis-checkpoint.ipynb	scheng1992/Data_Assimilation
Results by model	relu = df_res[df_res.activation == "relu"] lrelu = df_res[df_res.activation == "lrelu"] plt.scatter('model', "valid_loss", data=relu, marker="+", color='r') plt.scatter('model', "valid_loss", data=lrelu, marker="+", color='g') plt.ylabel("Loss") plt.xlabel("Model") plt.ylim(16000, 70000) plt.legend(labels=["relu", "lrelu"]) plt.show() #investigate number of layers eps = 1e-5 reluNBN = df_res[(df_res.activation == "relu") & (abs(df_res.batch_norm - 0.) < eps)] reluBN = df_res[(df_res.activation == "relu") & (abs(df_res.batch_norm - 1.) < eps)] lreluNBN = df_res[(df_res.activation == "lrelu") & (abs(df_res.batch_norm - 0.0) < eps)] lreluBN = df_res[(df_res.activation == "lrelu") & (abs(df_res.batch_norm - 1.) < eps)] plt.scatter('model', "valid_loss", data=reluNBN, marker="+", color='r') plt.scatter('model', "valid_loss", data=reluBN, marker="+", color='g') plt.scatter('model', "valid_loss", data=lreluNBN, marker="o", color='r') plt.scatter('model', "valid_loss", data=lreluBN, marker="o", color='g') plt.ylabel("Loss") plt.xlabel("Model") plt.ylim(16000, 70000) plt.legend(labels=["relu, NBN", "relu, BN", "lrelu, NBN", "lrelu, BN"]) plt.show()	_____no_output_____	MIT	notebooks/.ipynb_checkpoints/CAE_zoo_analysis-checkpoint.ipynb	scheng1992/Data_Assimilation
It turns out that there are lots of duplicates in the above data (as a result of a bug in my code that was giving all models the same number of channels). So remove duplicates and go again: Remove Duplicates	#remove duplicates columns = list(df_res_original.columns) columns.remove("model") columns.remove("path") print(columns) df_res_new = df_res_original.loc[df_res_original.astype(str).drop_duplicates(subset=columns, keep="last").index] #df_res_new = df_res_original.drop_duplicates(subset=columns, keep="last") df_res_new.shape df_res = df_res_new df_res.shape ##Plot same graph again: #investigate number of layers relu6 = df_res[(df_res.activation == "relu") & (df_res.num_layers == 6)] relu11 = df_res[(df_res.activation == "relu") & (df_res.num_layers != 6)] lrelu6 = df_res[(df_res.activation == "lrelu") & (df_res.num_layers == 6)] lrelu11 = df_res[(df_res.activation == "lrelu") & (df_res.num_layers != 6)] plt.scatter('model', "valid_loss", data=relu6, marker="+", color='r') plt.scatter('model', "valid_loss", data=lrelu6, marker="+", color='g') plt.scatter('model', "valid_loss", data=relu11, marker="o", color='r') plt.scatter('model', "valid_loss", data=lrelu11, marker="o", color='g') plt.ylabel("Loss") plt.xlabel("Model") plt.ylim(16000, 60000) plt.legend(labels=["relu, 6", "lrelu, 6", "relu, not 6", "lrelu, not 6"]) plt.show()	_____no_output_____	MIT	notebooks/.ipynb_checkpoints/CAE_zoo_analysis-checkpoint.ipynb	scheng1992/Data_Assimilation

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