markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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node features seem very sensitive perturb topology | # keep backup
backup = data.edge_index.clone()
backup
perturb_data_list = []
for i in range(1000):
# clone original data
pData = data.clone()
# noise parameters
noEdgeSwap = 3
# create edges
edges = pData.edge_index.T.tolist()
edges = np.array(edges)
edges = [(x[0][0], x[0][1], {"... | _____no_output_____ | MIT | examples/lsc/pcqm4m/.ipynb_checkpoints/triplet-loss-checkpoint.ipynb | edwardelson/ogb |
Copyright Netherlands eScience Center and Centrum Wiskunde & Informatica ** Function : Emotion recognition and forecast with BBConvLSTM** ** Author : Yang Liu** ** Contributor : Tianyi Zhang (Centrum Wiskunde & Informatica)** Last Update : 2021.02.08 ** ** Last Update : 2021.02.12 ** ** Library : Pyt... | %matplotlib inline
import sys
import numbers
import pickle
# for data loading
import os
# for pre-processing and machine learning
import numpy as np
import csv
#import sklearn
from scipy.signal import resample
# for visualization
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm | _____no_output_____ | Apache-2.0 | tests/data_preview.ipynb | geek-yang/NEmo |
The testing device is Dell Inspirion 5680 with Intel Core i7-8700 x64 CPU and Nvidia GTX 1060 6GB GPU.Here is a benchmark about cpu v.s. gtx 1060 https://www.analyticsindiamag.com/deep-learning-tensorflow-benchmark-intel-i5-4210u-vs-geforce-nvidia-1060-6gb/ | #################################################################################
######### datapath ########
#################################################################################
# please specify data path
datapath = 'H:\\Creator_Zone\\Script_craft\\NE... | _____no_output_____ | Apache-2.0 | tests/data_preview.ipynb | geek-yang/NEmo |
Recurrent PPO landing using raw altimeter readings | import numpy as np
import os,sys
sys.path.append('../../../RL_lib/Agents/PPO')
sys.path.append('../../../RL_lib/Utils')
sys.path.append('../../../Mars3dof_env')
sys.path.append('../../../Mars_DTM')
%load_ext autoreload
%load_ext autoreload
%autoreload 2
%matplotlib nbagg
import os
print(os.getcwd())
%%html
<style>
.... | _____no_output_____ | MIT | Experiments/Mars3DOF/Mars_landing_DTM/altimeter_v_mm3-120step.ipynb | CHEN-yongquan/RL-Meta-Learning-ACTA |
Optimize Policy | from env import Env
import env_utils as envu
from dynamics_model import Dynamics_model
from lander_model import Lander_model
from ic_gen2 import Landing_icgen
import rl_utils
from arch_policy_vf import Arch
from model import Model
from policy import Policy
from value_function import Value_function
import pcm_model_n... | _____no_output_____ | MIT | Experiments/Mars3DOF/Mars_landing_DTM/altimeter_v_mm3-120step.ipynb | CHEN-yongquan/RL-Meta-Learning-ACTA |
Test Policy with Realistic Noise | policy.test_mode=True
env.test_policy_batch(agent,1000,print_every=100)
len(lander_model.trajectory_list)
traj_list = lander_model.trajectory_list[0:100]
len(traj_list)
np.save(fname + '_100traj',traj_list)
envu.plot_rf_vf(env.rl_stats.history) | _____no_output_____ | MIT | Experiments/Mars3DOF/Mars_landing_DTM/altimeter_v_mm3-120step.ipynb | CHEN-yongquan/RL-Meta-Learning-ACTA |
Flowers classifier using Transfer Learning and tf.dataAccuracy : 0.9090909090909091Classification Report precision recall f1-score support 0 0.96429 0.90000 0.93103 60 1 0.88750 0.98611 0.93421 72 2 0.81538 0.89831 0.85484 59 ... | #"""
# Google Collab specific stuff....
from google.colab import drive
drive.mount('/content/drive')
import os
!ls "/content/drive/My Drive"
USING_COLLAB = True
%tensorflow_version 2.x
#"""
# Setup sys.path to find MachineLearning lib directory
try: USING_COLLAB
except NameError: USING_COLLAB = False
%load_ext auto... | _____no_output_____ | MIT | 1-Flowers/FlowersTransfer-TF-Data-V1.ipynb | bo9zbo9z/MachineLearning |
Examine and understand data | # GLOBALS/CONFIG ITEMS
# Set root directory path to data
if USING_COLLAB:
ROOT_PATH = "/content/drive/My Drive/ImageData/Flowers" ###### CHANGE FOR SPECIFIC ENVIRONMENT
else:
ROOT_PATH = "/Users/john/Documents/ImageData/Flowers" ###### CHANGE FOR SPECIFIC ENVIRONMENT
# Establish global dictionary
pa... | _____no_output_____ | MIT | 1-Flowers/FlowersTransfer-TF-Data-V1.ipynb | bo9zbo9z/MachineLearning |
Build an input pipeline |
def get_label(file_path):
# convert the path to a list of path components
parts = tf.strings.split(file_path, os.path.sep)
# The second to last is the class-directory
return parts[-2] == parms.CLASS_NAMES
def decode_image(image):
# convert the compressed string to a 3D uint8 tensor
image = tf.... | _____no_output_____ | MIT | 1-Flowers/FlowersTransfer-TF-Data-V1.ipynb | bo9zbo9z/MachineLearning |
Build model- add and validate pretrained model as a baseline | # Create any call backs for training...These are the most common.
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, CSVLogger
reduce_lr = ReduceLROnPlateau(monitor='loss', patience=2, verbose=1, min_lr=1e-6)
earlystopper = EarlyStopping(patience=8, verbose=1)
checkpointer = Mod... | _____no_output_____ | MIT | 1-Flowers/FlowersTransfer-TF-Data-V1.ipynb | bo9zbo9z/MachineLearning |
Train model | # Train model
steps_per_epoch = np.ceil(train_len // parms.BATCH_SIZE) # set step sizes based on train & batch
validation_steps = np.ceil(val_len // parms.BATCH_SIZE) # set step sizes based on val & batch
model = build_model(parms)
model = compile_model(parms, model)
history = model.fit(train_dataset,
... | _____no_output_____ | MIT | 1-Flowers/FlowersTransfer-TF-Data-V1.ipynb | bo9zbo9z/MachineLearning |
Validate model's predictions- Create actual_lables and predict_labels- Calculate Confusion Matrix & Accuracy- Display results | #Load saved model
from tensorflow.keras.models import load_model
def load_saved_model(model_path):
model = load_model(model_path)
print("loaded: ", model_path)
return model
model = load_saved_model(parms.MODEL_PATH)
# Use model to generate predicted labels and probabilities
#labels, predict_labels, predic... | _____no_output_____ | MIT | 1-Flowers/FlowersTransfer-TF-Data-V1.ipynb | bo9zbo9z/MachineLearning |
Copyright 2021 The TensorFlow Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Human Pose Classification with MoveNet and TensorFlow LiteThis notebook teaches you how to train a pose classification model using MoveNet and TensorFlow Lite. The result is a new TensorFlow Lite model that accepts the output from the MoveNet model as its input, and outputs a pose classification, such as the name of a... | !pip install -q opencv-python
import csv
import cv2
import itertools
import numpy as np
import pandas as pd
import os
import sys
import tempfile
import tqdm
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow impor... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Code to run pose estimation using MoveNet | #@title Functions to run pose estimation with MoveNet
#@markdown You'll download the MoveNet Thunder model from [TensorFlow Hub](https://www.google.com/url?sa=D&q=https%3A%2F%2Ftfhub.dev%2Fs%3Fq%3Dmovenet), and reuse some inference and visualization logic from the [MoveNet Raspberry Pi (Python)](https://github.com/ten... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Part 1: Preprocess the input imagesBecause the input for our pose classifier is the *output* landmarks from the MoveNet model, we need to generate our training dataset by running labeled images through MoveNet and then capturing all the landmark data and ground truth labels into a CSV file.The dataset we've provided f... | is_skip_step_1 = False #@param ["False", "True"] {type:"raw"} | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
(Optional) Upload your own pose dataset | use_custom_dataset = False #@param ["False", "True"] {type:"raw"}
dataset_is_split = False #@param ["False", "True"] {type:"raw"} | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
If you want to train the pose classifier with your own labeled poses (they can be any poses, not just yoga poses), follow these steps:1. Set the above `use_custom_dataset` option to **True**.2. Prepare an archive file (ZIP, TAR, or other) that includes a folder with your images dataset. The folder must include sorted i... | #@markdown Be sure you run this cell. It's hiding the `split_into_train_test()` function that's called in the next code block.
import os
import random
import shutil
def split_into_train_test(images_origin, images_dest, test_split):
"""Splits a directory of sorted images into training and test sets.
Args:
ima... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
**Note:** If you're using `split_into_train_test()` to split the dataset, it expects all images to be PNG, JPEG, or BMP—it ignores other file types. Download the yoga dataset | if not is_skip_step_1 and not use_custom_dataset:
!wget -O yoga_poses.zip http://download.tensorflow.org/data/pose_classification/yoga_poses.zip
!unzip -q yoga_poses.zip -d yoga_cg
IMAGES_ROOT = "yoga_cg" | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Preprocess the `TRAIN` dataset | if not is_skip_step_1:
images_in_train_folder = os.path.join(IMAGES_ROOT, 'train')
images_out_train_folder = 'poses_images_out_train'
csvs_out_train_path = 'train_data.csv'
preprocessor = MoveNetPreprocessor(
images_in_folder=images_in_train_folder,
images_out_folder=images_out_train_folder,
... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Preprocess the `TEST` dataset | if not is_skip_step_1:
images_in_test_folder = os.path.join(IMAGES_ROOT, 'test')
images_out_test_folder = 'poses_images_out_test'
csvs_out_test_path = 'test_data.csv'
preprocessor = MoveNetPreprocessor(
images_in_folder=images_in_test_folder,
images_out_folder=images_out_test_folder,
csvs_out... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Part 2: Train a pose classification model that takes the landmark coordinates as input, and output the predicted labels.You'll build a TensorFlow model that takes the landmark coordinates and predicts the pose class that the person in the input image performs. The model consists of two submodels:* Submodel 1 calculate... | # Download the preprocessed CSV files which are the same as the output of step 1
if is_skip_step_1:
!wget -O train_data.csv http://download.tensorflow.org/data/pose_classification/yoga_train_data.csv
!wget -O test_data.csv http://download.tensorflow.org/data/pose_classification/yoga_test_data.csv
csvs_out_train_... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Load the preprocessed CSVs into `TRAIN` and `TEST` datasets. | def load_pose_landmarks(csv_path):
"""Loads a CSV created by MoveNetPreprocessor.
Returns:
X: Detected landmark coordinates and scores of shape (N, 17 * 3)
y: Ground truth labels of shape (N, label_count)
classes: The list of all class names found in the dataset
dataframe: The CSV loaded as a Pan... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Load and split the original `TRAIN` dataset into `TRAIN` (85% of the data) and `VALIDATE` (the remaining 15%). | # Load the train data
X, y, class_names, _ = load_pose_landmarks(csvs_out_train_path)
# Split training data (X, y) into (X_train, y_train) and (X_val, y_val)
X_train, X_val, y_train, y_val = train_test_split(X, y,
test_size=0.15)
# Load the test data
X_test, y_test, _,... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Define functions to convert the pose landmarks to a pose embedding (a.k.a. feature vector) for pose classificationNext, convert the landmark coordinates to a feature vector by:1. Moving the pose center to the origin.2. Scaling the pose so that the pose size becomes 13. Flattening these coordinates into a feature vecto... | def get_center_point(landmarks, left_bodypart, right_bodypart):
"""Calculates the center point of the two given landmarks."""
left = tf.gather(landmarks, left_bodypart.value, axis=1)
right = tf.gather(landmarks, right_bodypart.value, axis=1)
center = left * 0.5 + right * 0.5
return center
def get_pose_size... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Define a Keras model for pose classificationOur Keras model takes the detected pose landmarks, then calculates the pose embedding and predicts the pose class. | # Define the model
inputs = tf.keras.Input(shape=(51))
embedding = landmarks_to_embedding(inputs)
layer = keras.layers.Dense(128, activation=tf.nn.relu6)(embedding)
layer = keras.layers.Dropout(0.5)(layer)
layer = keras.layers.Dense(64, activation=tf.nn.relu6)(layer)
layer = keras.layers.Dropout(0.5)(layer)
outputs = ... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Draw the confusion matrix to better understand the model performance | def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""Plots the confusion matrix."""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confus... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
(Optional) Investigate incorrect predictionsYou can look at the poses from the `TEST` dataset that were incorrectly predicted to see whether the model accuracy can be improved.Note: This only works if you have run step 1 because you need the pose image files on your local machine to display them. | if is_skip_step_1:
raise RuntimeError('You must have run step 1 to run this cell.')
# If step 1 was skipped, skip this step.
IMAGE_PER_ROW = 3
MAX_NO_OF_IMAGE_TO_PLOT = 30
# Extract the list of incorrectly predicted poses
false_predict = [id_in_df for id_in_df in range(len(y_test)) \
if y_pred_label... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Part 3: Convert the pose classification model to TensorFlow LiteYou'll convert the Keras pose classification model to the TensorFlow Lite format so that you can deploy it to mobile apps, web browsers and IoT devices. When converting the model, you'll apply [dynamic range quantization](https://www.tensorflow.org/lite/p... | converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
print('Model size: %dKB' % (len(tflite_model) / 1024))
with open('pose_classifier.tflite', 'wb') as f:
f.write(tflite_model) | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Then you'll write the label file which contains mapping from the class indexes to the human readable class names. | with open('pose_labels.txt', 'w') as f:
f.write('\n'.join(class_names)) | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
As you've applied quantization to reduce the model size, let's evaluate the quantized TFLite model to check whether the accuracy drop is acceptable. | def evaluate_model(interpreter, X, y_true):
"""Evaluates the given TFLite model and return its accuracy."""
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
# Run predictions on all given poses.
y_pred = []
for i in range(len(y_true)):
... | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
Now you can download the TFLite model (`pose_classifier.tflite`) and the label file (`pose_labels.txt`) to classify custom poses. See the [Android](https://github.com/tensorflow/examples/tree/master/lite/examples/pose_estimation/android) and [Python/Raspberry Pi](https://github.com/tensorflow/examples/tree/master/lite/... | !zip pose_classifier.zip pose_labels.txt pose_classifier.tflite
# Download the zip archive if running on Colab.
try:
from google.colab import files
files.download('pose_classifier.zip')
except:
pass | _____no_output_____ | Apache-2.0 | site/en-snapshot/lite/tutorials/pose_classification.ipynb | Icecoffee2500/docs-l10n |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/content/sample_data/california_housing_test.csv')
test = pd.read_csv('/content/sample_data/california_housing_train.csv')
train.head()
test.head()
train.describe()
train.hist(figsize=(15,13), grid=False, b... | _____no_output_____ | MIT | california_housing.ipynb | crazrycoin/Open-source | |
In this tutorial, we will learn how to plot a variable "Boundary layer height" for a particular output of WRF model.Referrence: https://wrf-python.readthedocs.io/en/latest/index.html 1. Import libraries | # Loading necessary libraries
import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature
from wrf import (to_np, getvar, smooth2d, get_cartopy, cartopy_xlim,
cartopy_ylim, l... | _____no_output_____ | MIT | notebook/06 WRF Python - Boundary layer height plot.ipynb | sonnymetvn/Basic-Python-for-Meteorology |
2. Download data | # specify where is the location of the data
path_in = "data/"
path_out = "./"
# Open the NetCDF file
ncfile = Dataset(path_in + 'wrfout_d01_2016-05-09_00^%00^%00') | _____no_output_____ | MIT | notebook/06 WRF Python - Boundary layer height plot.ipynb | sonnymetvn/Basic-Python-for-Meteorology |
3. Take out the variables | # Get the boundary layer height
PBLH = getvar(ncfile, "PBLH")
print(PBLH.dims) | ('south_north', 'west_east')
| MIT | notebook/06 WRF Python - Boundary layer height plot.ipynb | sonnymetvn/Basic-Python-for-Meteorology |
4. Plotting | PBLH.plot() | _____no_output_____ | MIT | notebook/06 WRF Python - Boundary layer height plot.ipynb | sonnymetvn/Basic-Python-for-Meteorology |
This notebook shows you how to create and query a table or DataFrame loaded from data stored in Azure Blob storage. | from pyspark.sql.functions import lit
from pyspark.sql.types import BinaryType,StringType
from pyspark.sql import SparkSession | _____no_output_____ | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
Step 1: Set the data location and typeThere are two ways to access Azure Blob storage: account keys and shared access signatures (SAS).To get started, we need to set the location and type of the file. | file_location = "256_sampledata/" | _____no_output_____ | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
Step 2: Read the dataNow that we have specified our file metadata, we can create a DataFrame. Notice that we use an *option* to specify that we want to infer the schema from the file. We can also explicitly set this to a particular schema if we have one already.First, let's create a DataFrame in Python. | ! ls -l "256_sampledata"
# start Spark session:
spark = SparkSession \
.builder \
.appName("Marhselling Image data") \
.config("spark.memory.offHeap.enabled",True) \
.config("spark.memory.offHeap.size","30g")\
.getOrCreate()
spark.sql("set spark.sql.files.ignoreCorruptFiles=true")
df = spark.read... | _____no_output_____ | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
preprocess 1. Extract labels 2. Extract size 3. transform labels to index Regex expressionNotice that every path file can be different, you will need to tweak the actual regex experssion to fit your file path. for that, take a look at an example of the file path and experiement with a [regex calculator](https://regex... | df.select("path").show(5, truncate=False)
import io
import numpy as np
import pandas as pd
import uuid
from pyspark.sql.functions import col, pandas_udf, regexp_extract
from PIL import Image
def extract_label(path_col):
"""Extract label category number from file path using built-in sql function"""
#([^/]+)
retu... | +--------------------+-------------+------------+--------------------+
| path| label| size| content|
+--------------------+-------------+------------+--------------------+
|file:/home/jovyan...| 249.yo-yo|{1500, 1500}|[FF D8 FF E0 00 1...|
|file:/home/jovyan...|196.spaghetti|... | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
Transform label to index 1st way - the python way | labels = images_w_label_size.select(col("label")).distinct().collect()
label_to_idx = {label: index for index,(label,) in enumerate(sorted(labels))}
num_classes = len(label_to_idx)
@pandas_udf("long")
def get_label_idx(labels):
return labels.map(lambda label: label_to_idx[label])
labels_idx = images_w_label_size.s... | +-------------+-----------+--------------------+--------------------+------------+
| label|label_index| content| path| size|
+-------------+-----------+--------------------+--------------------+------------+
| 249.yo-yo| 3|[FF D8 FF E0 00 1...|file:/home/jovyan...|{1... | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
2nd way - the mllib way | from pyspark.ml.feature import StringIndexer
indexer = StringIndexer(inputCol="label", outputCol="label_index")
indexed = indexer.fit(images_w_label_size).transform(images_w_label_size)
indexed.show(10)
indexed.select("label_index").distinct().collect() | _____no_output_____ | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
3rd way - from the label itself | def extract_index_from_label(label):
"""Extract index from label"""
return regexp_extract(label,"^([^.]+)",1)
labels_idx = images_w_label_size.select(
col("label"),
extract_index_from_label(col("label")).alias("label_index"),
col("content"),
col("path"),
col("size"))
labels_idx.show(5,trunca... | _____no_output_____ | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
Step 3: Feature EngineeringExtracting greyscale images.Greyscale is used as an example of feature we might want to extract. | df.printSchema() | root
|-- path: string (nullable = true)
|-- label: string (nullable = true)
|-- size: struct (nullable = true)
| |-- width: integer (nullable = true)
| |-- height: integer (nullable = true)
|-- content: binary (nullable = true)
|-- label_index: double (nullable = false)
| Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
calculate average image size for each category1. flat the column into two columns2. calculate average size for category3. resize according to average. | # 1st step - flatten the struact
flattened = df.withColumn('width', col('size')['width'])
flattened = flattened.withColumn('height', col('size')['height'])
flattened.select('width','height').show(3, truncate = False)
# 2 - calculate average size for category
import pandas as pd
from pyspark.sql.functions import pandas... | +------------------+
|pandas_mean(width)|
+------------------+
| 165992|
+------------------+
+-------------+------------------+
| label|pandas_mean(width)|
+-------------+------------------+
|196.spaghetti| 39019|
| 249.yo-yo| 40944|
| 234.tweezer| 34513|
| ... | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
Extract greyscale | # Sample python native function that can do additional processing - expects pandas df as input and returns pandas df as output.
def add_grayscale_img(input_df):
# Set up return frame. In this case I'll have a row per passed in row. You could be aggregating down to a single image, slicing
# out columns,or just abo... | _____no_output_____ | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
Test on small data | pd_df = limited_df.limit(5).toPandas()
print(pd_df.columns)
limited_df = None | _____no_output_____ | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
Make sure function works correctly |
# Some testing code
test_df = pd_df.copy()
add_grayscale_img(test_df)
print(test_df['grayscale_image'])
from PIL import ImageFilter
# Sample python native function that can do additional processing - expects pandas df as input and returns pandas df as output.
def add_laplas(input_df):
# Set up return frame. In th... | _____no_output_____ | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
Full Dataset | output_df.show(2, truncate=True)
output_df.printSchema() | root
|-- path: string (nullable = true)
|-- label: string (nullable = true)
|-- size: struct (nullable = true)
| |-- width: integer (nullable = true)
| |-- height: integer (nullable = true)
|-- content: binary (nullable = true)
|-- label_index: double (nullable = false)
|-- grayscale_image: binary (nullab... | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
Step 5: scale the imageFrom the size column, we notice that caltech_256 image size highly varay. To proced with the process, we need to scale the images to have a unannimous size. For tha we will use Spark UDFs with PIL.This is a must do part of normalizing and preprocessing image data. | from pyspark.sql.types import BinaryType, IntegerType
from pyspark.sql.functions import udf
img_size = 224
def scale_image(image_bytes):
try:
image = Image.open(io.BytesIO(image_bytes)).resize([img_size, img_size])
return image.tobytes()
except:
return None
array = output_df.select("conten... | root
|-- label_index: double (nullable = false)
|-- content: binary (nullable = true)
| Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
Step 4: Save and Avoid small files problemSave the image data into a file format where you can query and process at scaleSaving the dataset with the greyscale. Repartition and save to **parquet** | # incase you are running on a distributed environment, with a large dataset, it's a good idea to partition t
# save the data:
save_path_augmented = "images_data/silver/augmented"
# Images data is already compressed so we turn off parquet compression
compression = spark.conf.get("spark.sql.parquet.compression.codec")
... | _____no_output_____ | Apache-2.0 | notebooks/ch04-05_Caltech256 - Loading and process Images Data.ipynb | adipolak/ml-with-apache-spark |
Histogram* Create a histogram to visualize the most common salary ranges for employees. | # x_axis = sal_title_group_clean['salary']
# y_axis =
plt.hist(emp_title_merged['salary'], color="red" )
plt.title('Salary Ranges for Employees')
plt.xlabel('Salary Range ($)')
plt.ylabel('Employee Count')
plt.grid(alpha=0.5)
plt.show()
plt.tight_layout() | _____no_output_____ | ADSL | EmployeeSQL/Working files/SQL BONUS.ipynb | key12pat34/SQL-challenge-hw7 |
Bar Chart* Create a bar chart of average salary by title. | x_axis = sal_title_group_clean['title']
y_axis = sal_title_group_clean['salary']
plt.bar(x_axis, y_axis, align = 'center', alpha=0.75, color = ['red','green','blue', 'black', 'orange', 'grey', 'purple'])
plt.xticks(rotation = 'vertical')
plt.title("Average Salary by Title")
plt.xlabel("Employee Titles")
plt.ylabel("S... | _____no_output_____ | ADSL | EmployeeSQL/Working files/SQL BONUS.ipynb | key12pat34/SQL-challenge-hw7 |
Exercícios | data = [[1,2,3],[4,5,6],[7,8,9]]
list('CBA')
list('ZYX')
df = pd.DataFrame(data, list('zyx'), list('cba'))
df
df.sort_index()
df.sort_index(axis = 1)
df | _____no_output_____ | MIT | Pandas/Dados/extras/extras/Organizando DataFrames (Sort).ipynb | lingsv/alura_ds |
JWST Pipeline Validation Testing Notebook: Calwebb_detector1, reset step for MIRI **Instruments Affected**: MIRI Table of Contents [Imports](imports_ID) [Introduction](intro_ID) [Get Documentaion String for Markdown Blocks](markdown_from_docs) [Loading Data](data_ID) [Run JWST Pipeline](pipeline_ID) [Create Figu... | from ci_watson.artifactory_helpers import get_bigdata
import inspect
from IPython.display import Markdown
from jwst.dq_init import DQInitStep
from jwst.reset import ResetStep
from jwst.datamodels import RampModel
import matplotlib.pyplot as plt
import numpy as np | _____no_output_____ | BSD-3-Clause | jwst_validation_notebooks/reset/jwst_reset_miri_test/jwst_reset_miri_testing.ipynb | jbhagan/jwst_validation_notebooks |
IntroductionFor this test we are using the reset step in the calwebb_detector1 pipeline. For MIRI exposures, the initial groups in each integration suffer from two effects related to the resetting of the detectors. The first effect is that the first few groups after a reset do not fall on the expected linear accumulat... | # Get raw python docstring
raw = inspect.getdoc(ResetStep)
# To convert to markdown, you need convert line breaks from \n to <br />
markdown_text = "<br />".join(raw.split("\n"))
# Here you can format markdown as an output using the Markdown method.
Markdown("""
# ResetStep
---
{}
""".format(markdown_text)) | _____no_output_____ | BSD-3-Clause | jwst_validation_notebooks/reset/jwst_reset_miri_test/jwst_reset_miri_testing.ipynb | jbhagan/jwst_validation_notebooks |
Loading DataThe data used to test this step is a dark data file taken as part of pre-launch ground testing. The original file name is MIRV00330001001P0000000002101_1_493_SE_2017-09-07T15h14m25.fits that was renamed to jw02201001001_01101_00001_MIRIMAGE_uncal.fits with a script that updates the file to put it in pipeli... | filename = get_bigdata('jwst_validation_notebooks',
'validation_data',
'reset',
'reset_miri_test',
'jw02201001001_01101_00001_MIRIMAGE_uncal.fits') | _____no_output_____ | BSD-3-Clause | jwst_validation_notebooks/reset/jwst_reset_miri_test/jwst_reset_miri_testing.ipynb | jbhagan/jwst_validation_notebooks |
Run JWST PipelineTake the initial input file and run it through both dq_init and reset to get the before and after correction versions of the data to run.[Top of Page](title_ID) | preim = DQInitStep.call(filename)
postim = ResetStep.call(preim) | _____no_output_____ | BSD-3-Clause | jwst_validation_notebooks/reset/jwst_reset_miri_test/jwst_reset_miri_testing.ipynb | jbhagan/jwst_validation_notebooks |
Show plots and take statistics before and after correctionFor a specific pixel in the dark data:1. Plot the ramps before and after the correction to see if the initial frame values are more in line with the rest of the ramp.2. Fit a line to the ramps and calculate the slope and residuals. The slope should be closer to... | # set input variables
print('Shape of data cube: integrations, groups, ysize, xsize ',preim.shape)
xval = 650
yval = 550
framenum = 20 # number of frames to plot (reset only corrects first few frames in cube)
intsnum = 3 # number of integrations to plot (3 should show reset and not crowd)
# put data into prope... | _____no_output_____ | BSD-3-Clause | jwst_validation_notebooks/reset/jwst_reset_miri_test/jwst_reset_miri_testing.ipynb | jbhagan/jwst_validation_notebooks |
First plot should show that after the correction, the drop at the early part of the ramp has evened out to resemble the data in the rest of the ramp. | # Plot frames vs. counts for a dark pixel before and after correction
# loop through integrations
for i in range(0, intsnum):
# get locations of flagged pixels within the ramps
ramp1 = impre.data[i, 0:framenum, yval, xval]
ramp2 = impost.data[i, 0:framenum, yval, xval]
# plot ramps of selected pixels... | _____no_output_____ | BSD-3-Clause | jwst_validation_notebooks/reset/jwst_reset_miri_test/jwst_reset_miri_testing.ipynb | jbhagan/jwst_validation_notebooks |
Take a single pixel in the file, before and after the correction, and fit a line to them. After the correction, for a dark, the slope should be closer to zero and the residuals should be much lower. | # get array of frame numbers and choose ramps for selected pixel
frames = np.arange(0, framenum)
preramp = impre.data[0, 0:framenum, yval, xval]
postramp = impost.data[0, 0:framenum, yval, xval]
# get slopes of selected pixel before and after correction and see if it is more linear
fit = np.polyfit(frames, preramp, ... | _____no_output_____ | BSD-3-Clause | jwst_validation_notebooks/reset/jwst_reset_miri_test/jwst_reset_miri_testing.ipynb | jbhagan/jwst_validation_notebooks |
Plot the residuals for the linear fit before and after correction for the specified pixel to see if the plotted ramp is flatter after the correction. | # show line plus residual for 1st int
yfit = np.polyval(fit[0], frames)
yfitcorr = np.polyval(fitpost[0], frames)
plt.title('Residuals for ramp (single pixel) before and after reset')
plt.xlabel('Frames')
plt.ylabel('Residual: linear fit - data')
plt.plot(frames, yfit - preramp, label='raw variance')
plt.plot(frames, ... | _____no_output_____ | BSD-3-Clause | jwst_validation_notebooks/reset/jwst_reset_miri_test/jwst_reset_miri_testing.ipynb | jbhagan/jwst_validation_notebooks |
Basic Optimization | def f(x):
return (x-3)**2
sp.optimize.minimize(f,2)
sp.optimize.minimize(f,2).x
sp.optimize.minimize(f,2).fun
sp.optimize.minimize? | _____no_output_____ | MIT | python/matplotlib/vector/basic/scipy.ipynb | karng87/nasm_game |
$$ f(x,y) = (x-1)^2 + (y-2.5)^2 $$$$ x - 2y + 2 \geq 0 \\ -x - 2y + 6 \geq 0 \\ -x + 2y + 2 \geq 0 \\ x \geq 0 \\ y \geq 0$$ | def f(x,y):
return (x-1)**2 + (y-2.5)**2
def g(x,y):
return x - 2*y + 2
def h(x,y):
return -x - 2*y + 6
def k(x,y):
return -x + 2*y +2
x = np.linspace(0,5,100)
x,y = np.meshgrid(x,x)
z = f(x,y)
g = g(x,y)
h = h(x,y)
k = k(x,y)
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.plot_surface(x... | _____no_output_____ | MIT | python/matplotlib/vector/basic/scipy.ipynb | karng87/nasm_game |
interpolate | x = np.linspace(0,10,10)
y = x**2 * np.sin(x)
fig = plt.figure()
ax = fig.add_subplot()
plt.scatter(x,y)
f = sp.interpolate.interp1d(x,y,kind='linear')
f = sp.interpolate.interp1d(x,y,kind='cubic')
x_dense = np.linspace(0,10,100)
y_dense = f(x_dense)
ax.plot(x_dense,y_dense)
def f(x):
return x**2 +5
sp.integrate.q... | _____no_output_____ | MIT | python/matplotlib/vector/basic/scipy.ipynb | karng87/nasm_game |
{glue:text}`nteract_github_org`**Activity from {glue:}`nteract_start` to {glue:}`nteract_stop`** | from datetime import date
from dateutil.relativedelta import relativedelta
from myst_nb import glue
import seaborn as sns
import pandas as pd
import numpy as np
import altair as alt
from markdown import markdown
from IPython.display import Markdown
from ipywidgets.widgets import HTML, Tab
from ipywidgets import widgets... | _____no_output_____ | BSD-3-Clause | monthly_update/generated/book/nteract.ipynb | choldgraf/jupyter-activity-snapshot |
Load dataLoad and clean up the data | from pathlib import Path
path_data = Path("../data")
comments = pd.read_csv(path_data.joinpath('comments.csv'), index_col=None).drop_duplicates()
issues = pd.read_csv(path_data.joinpath('issues.csv'), index_col=None).drop_duplicates()
prs = pd.read_csv(path_data.joinpath('prs.csv'), index_col=None).drop_duplicates()
f... | _____no_output_____ | BSD-3-Clause | monthly_update/generated/book/nteract.ipynb | choldgraf/jupyter-activity-snapshot |
Merged Pull requestsHere's an analysis of **merged pull requests** across each of the repositories in the Jupyterecosystem. | merged = prs.query('state == "MERGED" and closedAt > @start_date and closedAt < @stop_date')
prs_by_repo = merged.groupby(['org', 'repo']).count()['author'].reset_index().sort_values(['org', 'author'], ascending=False)
alt.Chart(data=prs_by_repo, title=f"Merged PRs in the last {n_days} days").mark_bar().encode(
x=a... | _____no_output_____ | BSD-3-Clause | monthly_update/generated/book/nteract.ipynb | choldgraf/jupyter-activity-snapshot |
Authoring and merging stats by repositoryLet's see who has been doing most of the PR authoring and merging. The PR author is generally theperson that implemented a change in the repository (code, documentation, etc). The PR merger isthe person that "pressed the green button" and got the change into the main codebase. | # Prep our merging DF
merged_by_repo = merged.groupby(['repo', 'author'], as_index=False).agg({'id': 'count', 'authorAssociation': 'first'}).rename(columns={'id': "authored", 'author': 'username'})
closed_by_repo = merged.groupby(['repo', 'mergedBy']).count()['id'].reset_index().rename(columns={'id': "closed", "mergedB... | _____no_output_____ | BSD-3-Clause | monthly_update/generated/book/nteract.ipynb | choldgraf/jupyter-activity-snapshot |
IssuesIssues are **conversations** that happen on our GitHub repositories. Here's ananalysis of issues across the Jupyter organizations. | created = issues.query('state == "OPEN" and createdAt > @start_date and createdAt < @stop_date')
closed = issues.query('state == "CLOSED" and closedAt > @start_date and closedAt < @stop_date')
created_counts = created.groupby(['org', 'repo']).count()['number'].reset_index()
created_counts['org/repo'] = created_counts.a... | _____no_output_____ | BSD-3-Clause | monthly_update/generated/book/nteract.ipynb | choldgraf/jupyter-activity-snapshot |
Most-upvoted issues | thumbsup = issues.sort_values("thumbsup", ascending=False).head(25)
thumbsup = thumbsup[["title", "url", "number", "thumbsup", "repo"]]
text = []
for ii, irow in thumbsup.iterrows():
itext = f"- ({irow['thumbsup']}) {irow['title']} - {irow['repo']} - [#{irow['number']}]({irow['url']})"
text.append(itext)
text ... | _____no_output_____ | BSD-3-Clause | monthly_update/generated/book/nteract.ipynb | choldgraf/jupyter-activity-snapshot |
Commenters across repositoriesThese are commenters across all issues and pull requests in the last several days.These are colored by the commenter's association with the organization. For informationabout what these associations mean, [see this StackOverflow post](https://stackoverflow.com/a/28866914/1927102). | commentors = (
comments
.query("createdAt > @start_date and createdAt < @stop_date")
.groupby(['org', 'repo', 'author', 'authorAssociation'])
.count().rename(columns={'id': 'count'})['count']
.reset_index()
.sort_values(['org', 'count'], ascending=False)
)
n_plot = 50
charts = []
for ii, (iorg, ... | _____no_output_____ | BSD-3-Clause | monthly_update/generated/book/nteract.ipynb | choldgraf/jupyter-activity-snapshot |
First respondersFirst responders are the first people to respond to a new issue in one of the repositories.The following plots show first responders for recently-created issues. | first_comments = []
for (org, repo, issue_id), i_comments in comments.groupby(['org', 'repo', 'id']):
ix_min = pd.to_datetime(i_comments['createdAt']).idxmin()
first_comment = i_comments.loc[ix_min]
if isinstance(first_comment, pd.DataFrame):
first_comment = first_comment.iloc[0]
first_comments.... | _____no_output_____ | BSD-3-Clause | monthly_update/generated/book/nteract.ipynb | choldgraf/jupyter-activity-snapshot |
Recent activity A list of merged PRs by projectBelow is a tabbed readout of recently-merged PRs. Check out the title to get an idea for what theyimplemented, and be sure to thank the PR author for their hard work! | tabs = widgets.Tab(children=[])
for ii, ((org, repo), imerged) in enumerate(merged.query("repo in @use_repos").groupby(['org', 'repo'])):
merged_by = {}
pr_by = {}
issue_md = []
issue_md.append(f"#### Closed PRs for repo: [{org}/{repo}](https://github.com/{github_org}/{repo})")
issue_md.append("")
... | _____no_output_____ | BSD-3-Clause | monthly_update/generated/book/nteract.ipynb | choldgraf/jupyter-activity-snapshot |
A list of recent issuesBelow is a list of issues with recent activity in each repository. If they seem of interestto you, click on their links and jump in to participate! | # Add comment count data to issues and PRs
comment_counts = (
comments
.query("createdAt > @start_date and createdAt < @stop_date")
.groupby(['org', 'repo', 'id'])
.count().iloc[:, 0].to_frame()
)
comment_counts.columns = ['n_comments']
comment_counts = comment_counts.reset_index()
n_plot = 5
tabs = wid... | _____no_output_____ | BSD-3-Clause | monthly_update/generated/book/nteract.ipynb | choldgraf/jupyter-activity-snapshot |
Title HeatMap Element Dependencies Matplotlib Backends Matplotlib Bokeh | import numpy as np
import holoviews as hv
hv.extension('matplotlib') | _____no_output_____ | BSD-3-Clause | examples/reference/elements/matplotlib/HeatMap.ipynb | stuarteberg/holoviews |
``HeatMap`` visualises tabular data indexed by two key dimensions as a grid of colored values. This allows spotting correlations in multivariate data and provides a high-level overview of how the two variables are plotted.The data for a ``HeatMap`` may be supplied as 2D tabular data with one or more associated value di... | data = [(chr(65+i), chr(97+j), i*j) for i in range(5) for j in range(5) if i!=j]
hv.HeatMap(data).sort() | _____no_output_____ | BSD-3-Clause | examples/reference/elements/matplotlib/HeatMap.ipynb | stuarteberg/holoviews |
It is important to note that the data should be aggregated before plotting as the ``HeatMap`` cannot display multiple values for one coordinate and will simply use the first value it finds for each combination of x- and y-coordinates. | heatmap = hv.HeatMap([(0, 0, 1), (0, 0, 10), (1, 0, 2), (1, 1, 3)])
heatmap + heatmap.aggregate(function=np.max) | _____no_output_____ | BSD-3-Clause | examples/reference/elements/matplotlib/HeatMap.ipynb | stuarteberg/holoviews |
As the above example shows before aggregating the second value for the (0, 0) is ignored unless we aggregate the data first.To reveal the values of a ``HeatMap`` we can enable a ``colorbar`` and if you wish to have interactive hover information, you can use the hover tool in the [Bokeh backend](../bokeh/HeatMap.ipynb): | heatmap = hv.HeatMap((np.random.randint(0, 10, 100), np.random.randint(0, 10, 100),
np.random.randn(100), np.random.randn(100)), vdims=['z', 'z2']).redim.range(z=(-2, 2))
heatmap.opts(colorbar=True, fig_size=250) | _____no_output_____ | BSD-3-Clause | examples/reference/elements/matplotlib/HeatMap.ipynb | stuarteberg/holoviews |
決策樹學習 - 分類樹 (以RR Lyrae變星資料集為例)* [程式碼來源](http://www.astroml.org/book_figures/chapter9/fig_rrlyrae_decisiontree.htmlbook-fig-chapter9-fig-rrlyrae-decisiontree) | import numpy as np
from matplotlib import pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
#fetch_rrlyrae_combined?
X, y = fetch_rrlyrae_combined() # 合併RR Lyrae... | _____no_output_____ | MIT | notebooks/notebooks4ML/DecisionTreeClassifier_RRLyraeExample.ipynb | Astrohackers-TW/IANCUPythonMeetup |
Generating benchmark data with 2 covariates p=30 | import pandas as pd
import toytree as tt
import numpy as np
import anndata as ad
import os
import toyplot as tp
import toyplot.svg
import seaborn as sns
import benchmarks.scripts.tree_data_generation as tgen
# tree depth
d = 5
effect_sizes = [0.3, 0.5, 0.7, 0.9]
# number of effects
num_effects = 3
# baseline paramete... | /Users/johannes.ostner/opt/anaconda3/envs/scCODA_3/lib/python3.8/site-packages/anndata/_core/anndata.py:120: ImplicitModificationWarning: Transforming to str index.
warnings.warn("Transforming to str index.", ImplicitModificationWarning)
| BSD-3-Clause | benchmarks/2_covariates/generate_data_2_covariates.ipynb | bio-datascience/tascCODA_reproducibility |
GANs | %matplotlib inline
from fastai.gen_doc.nbdoc import *
from fastai import *
from fastai.vision import *
from fastai.vision.gan import * | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
GAN stands for [Generative Adversarial Nets](https://arxiv.org/pdf/1406.2661.pdf) and were invented by Ian Goodfellow. The concept is that we will train two models at the same time: a generator and a critic. The generator will try to make new images similar to the ones in our dataset, and the critic's job will try to c... | show_doc(GANLearner) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
This is the general constructor to create a GAN, you might want to use one of the factory methods that are easier to use. Create a GAN from [`data`](/vision.data.htmlvision.data), a `generator` and a `critic`. The [`data`](/vision.data.htmlvision.data) should have the inputs the `generator` will expect and the images w... | show_doc(GANLearner.from_learners) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
Directly creates a [`GANLearner`](/vision.gan.htmlGANLearner) from two [`Learner`](/basic_train.htmlLearner): one for the `generator` and one for the `critic`. The `switcher` and all `kwargs` will be passed to the initialization of [`GANLearner`](/vision.gan.htmlGANLearner) along with the following loss functions:- `lo... | show_doc(GANLearner.wgan) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
The Wasserstein GAN is detailed in [this article]. `switcher` and the `kwargs` will be passed to the [`GANLearner`](/vision.gan.htmlGANLearner) init, `clip`is the weight clipping. Switchers In any GAN training, you will need to tell the [`Learner`](/basic_train.htmlLearner) when to switch from generator to critic and ... | show_doc(FixedGANSwitcher, title_level=3)
show_doc(FixedGANSwitcher.on_train_begin)
show_doc(FixedGANSwitcher.on_batch_end)
show_doc(AdaptiveGANSwitcher, title_level=3)
show_doc(AdaptiveGANSwitcher.on_batch_end) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
Discriminative LR If you want to train your critic at a different learning rate than the generator, this will let you do it automatically (even if you have a learning rate schedule). | show_doc(GANDiscriminativeLR, title_level=3)
show_doc(GANDiscriminativeLR.on_batch_begin)
show_doc(GANDiscriminativeLR.on_step_end) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
Specific models | show_doc(basic_critic) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
This model contains a first 4 by 4 convolutional layer of stride 2 from `n_channels` to `n_features` followed by `n_extra_layers` 3 by 3 convolutional layer of stride 1. Then we put as many 4 by 4 convolutional layer of stride 2 with a number of features multiplied by 2 at each stage so that the `in_size` becomes 1. `k... | show_doc(basic_generator) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
This model contains a first 4 by 4 transposed convolutional layer of stride 1 from `noise_size` to the last numbers of features of the corresponding critic. Then we put as many 4 by 4 transposed convolutional layer of stride 2 with a number of features divided by 2 at each stage so that the image ends up being of heigh... | show_doc(gan_critic)
show_doc(GANTrainer) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
[`LearnerCallback`](/basic_train.htmlLearnerCallback) that will be responsible to handle the two different optimizers (one for the generator and one for the critic), and do all the work behind the scenes so that the generator (or the critic) are in training mode with parameters requirement gradients each time we switch... | show_doc(GANTrainer.switch) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
If `gen_mode` is left as `None`, just put the model in the other mode (critic if it was in generator mode and vice versa). | show_doc(GANTrainer.on_train_begin)
show_doc(GANTrainer.on_epoch_begin)
show_doc(GANTrainer.on_batch_begin)
show_doc(GANTrainer.on_backward_begin)
show_doc(GANTrainer.on_epoch_end)
show_doc(GANTrainer.on_train_end) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
Specific modules | show_doc(GANModule, title_level=3) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
If `gen_mode` is left as `None`, just put the model in the other mode (critic if it was in generator mode and vice versa). | show_doc(GANModule.switch)
show_doc(GANLoss, title_level=3)
show_doc(AdaptiveLoss, title_level=3)
show_doc(accuracy_thresh_expand) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
Data Block API | show_doc(NoisyItem, title_level=3)
show_doc(GANItemList, title_level=3) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
Inputs will be [`NoisyItem`](/vision.gan.htmlNoisyItem) of `noise_sz` while the default class for target is [`ImageItemList`](/vision.data.htmlImageItemList). | show_doc(GANItemList.show_xys)
show_doc(GANItemList.show_xyzs) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
Undocumented Methods - Methods moved below this line will intentionally be hidden | show_doc(GANLoss.critic)
show_doc(GANModule.forward)
show_doc(GANLoss.generator)
show_doc(NoisyItem.apply_tfms)
show_doc(AdaptiveLoss.forward)
show_doc(GANItemList.get)
show_doc(GANItemList.reconstruct)
show_doc(AdaptiveLoss.forward) | _____no_output_____ | Apache-2.0 | docs_src/vision.gan.ipynb | navjotts/fastai |
解析庫 | BeautifulSoup(markup, "html.parser")
BeautifulSoup(markup, "lxml")
BeautifulSoup(markup, "xml")
BeautifulSoup(markup, "html5lib") | _____no_output_____ | MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
基本使用 | #引入requests好爬取html檔案給bs4使用
import requests
response = requests.get('http://ntumail.cc.ntu.edu.tw')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'html.parser')
print(soup.prettify()) #會把html漂亮輸出
print(soup.title.string) |
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta content="text/html; charset=utf-8" http-equiv="Content-Type"/>
<title>
NTU Mail-臺灣大學電子郵件系統
</title>
<link href="images/style.cs... | MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
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