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Vertex constantsSetup up the following constants for Vertex:- `API_ENDPOINT`: The Vertex API service endpoint for dataset, model, job, pipeline and endpoint services.- `PARENT`: The Vertex location root path for dataset, model, job, pipeline and endpoint resources. | # API service endpoint
API_ENDPOINT = "{}-aiplatform.googleapis.com".format(REGION)
# Vertex location root path for your dataset, model and endpoint resources
PARENT = "projects/" + PROJECT_ID + "/locations/" + REGION | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
AutoML constantsSet constants unique to AutoML datasets and training:- Dataset Schemas: Tells the `Dataset` resource service which type of dataset it is.- Data Labeling (Annotations) Schemas: Tells the `Dataset` resource service how the data is labeled (annotated).- Dataset Training Schemas: Tells the `Pipeline` resou... | # Video Dataset type
DATA_SCHEMA = 'gs://google-cloud-aiplatform/schema/dataset/metadata/video_1.0.0.yaml'
# Video Labeling type
LABEL_SCHEMA = "gs://google-cloud-aiplatform/schema/dataset/ioformat/video_action_recognition_io_format_1.0.0.yaml"
# Video Training task
TRAINING_SCHEMA = "gs://google-cloud-aiplatform/schem... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Hardware AcceleratorsSet the hardware accelerators (e.g., GPU), if any, for prediction.Set the variable `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs alloca... | if os.getenv("IS_TESTING_DEPOLY_GPU"):
DEPLOY_GPU, DEPLOY_NGPU = (aip.AcceleratorType.NVIDIA_TESLA_K80, int(os.getenv("IS_TESTING_DEPOLY_GPU")))
else:
DEPLOY_GPU, DEPLOY_NGPU = (aip.AcceleratorType.NVIDIA_TESLA_K80, 1) | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Container (Docker) imageFor AutoML batch prediction, the container image for the serving binary is pre-determined by the Vertex prediction service. More specifically, the service will pick the appropriate container for the model depending on the hardware accelerator you selected. Machine TypeNext, set the machine typ... | if os.getenv("IS_TESTING_DEPLOY_MACHINE"):
MACHINE_TYPE = os.getenv("IS_TESTING_DEPLOY_MACHINE")
else:
MACHINE_TYPE = 'n1-standard'
VCPU = '4'
DEPLOY_COMPUTE = MACHINE_TYPE + '-' + VCPU
print('Deploy machine type', DEPLOY_COMPUTE) | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
TutorialNow you are ready to start creating your own AutoML video action recognition model. Set up clientsThe Vertex client library works as a client/server model. On your side (the Python script) you will create a client that sends requests and receives responses from the Vertex server.You will use different clients... | # client options same for all services
client_options = {"api_endpoint": API_ENDPOINT}
def create_dataset_client():
client = aip.DatasetServiceClient(
client_options=client_options
)
return client
def create_model_client():
client = aip.ModelServiceClient(
client_options=client_optio... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
DatasetNow that your clients are ready, your first step in training a model is to create a managed dataset instance, and then upload your labeled data to it. Create `Dataset` resource instanceUse the helper function `create_dataset` to create the instance of a `Dataset` resource. This function does the following:1. Us... | TIMEOUT = 90
def create_dataset(name, schema, labels=None, timeout=TIMEOUT):
start_time = time.time()
try:
dataset = aip.Dataset(display_name=name, metadata_schema_uri=schema, labels=labels)
operation = clients['dataset'].create_dataset(parent=PARENT, dataset=dataset)
print("Long runni... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Now save the unique dataset identifier for the `Dataset` resource instance you created. | # The full unique ID for the dataset
dataset_id = result.name
# The short numeric ID for the dataset
dataset_short_id = dataset_id.split('/')[-1]
print(dataset_id) | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Data preparationThe Vertex `Dataset` resource for video has some requirements for your data.- Videos must be stored in a Cloud Storage bucket.- Each video file must be in a video format (MPG, AVI, ...).- There must be an index file stored in your Cloud Storage bucket that contains the path and label for each video.- T... | IMPORT_FILES = ['gs://automl-video-demo-data/hmdb_golf_swing_train.csv', 'gs://automl-video-demo-data/hmdb_golf_swing_test.csv'] | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Quick peek at your dataYou will use a version of the Golf Swings dataset that is stored in a public Cloud Storage bucket, using a CSV index file.Start by doing a quick peek at the data. You count the number of examples by counting the number of rows in the CSV index file (`wc -l`) and then peek at the first few rows. | if 'IMPORT_FILES' in globals():
FILE = IMPORT_FILES[0]
else:
FILE = IMPORT_FILE
count = ! gsutil cat $FILE | wc -l
print("Number of Examples", int(count[0]))
print("First 10 rows")
! gsutil cat $FILE | head | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Import dataNow, import the data into your Vertex Dataset resource. Use this helper function `import_data` to import the data. The function does the following:- Uses the `Dataset` client.- Calls the client method `import_data`, with the following parameters: - `name`: The human readable name you give to the `Dataset` r... | def import_data(dataset, gcs_sources, schema):
config = [{
'gcs_source': {'uris': gcs_sources},
'import_schema_uri': schema
}]
print("dataset:", dataset_id)
start_time = time.time()
try:
operation = clients['dataset'].import_data(name=dataset_id, import_configs=config)
... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Train the modelNow train an AutoML video action recognition model using your Vertex `Dataset` resource. To train the model, do the following steps:1. Create an Vertex training pipeline for the `Dataset` resource.2. Execute the pipeline to start the training. Create a training pipelineYou may ask, what do we use a pip... | def create_pipeline(pipeline_name, model_name, dataset, schema, task):
dataset_id = dataset.split('/')[-1]
input_config = {'dataset_id': dataset_id,
'fraction_split': {
'training_fraction': 0.8,
'test_fraction': 0.2
}}
... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Construct the task requirementsNext, construct the task requirements. Unlike other parameters which take a Python (JSON-like) dictionary, the `task` field takes a Google protobuf Struct, which is very similar to a Python dictionary. Use the `json_format.ParseDict` method for the conversion.The minimal fields you need ... | PIPE_NAME = "golf_pipe-" + TIMESTAMP
MODEL_NAME = "golf_model-" + TIMESTAMP
task = json_format.ParseDict({'model_type': "CLOUD",
}, Value())
response = create_pipeline(PIPE_NAME, MODEL_NAME, dataset_id, TRAINING_SCHEMA, task) | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Now save the unique identifier of the training pipeline you created. | # The full unique ID for the pipeline
pipeline_id = response.name
# The short numeric ID for the pipeline
pipeline_short_id = pipeline_id.split('/')[-1]
print(pipeline_id) | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Get information on a training pipelineNow get pipeline information for just this training pipeline instance. The helper function gets the job information for just this job by calling the the job client service's `get_training_pipeline` method, with the following parameter:- `name`: The Vertex fully qualified pipeline... | def get_training_pipeline(name, silent=False):
response = clients['pipeline'].get_training_pipeline(name=name)
if silent:
return response
print("pipeline")
print(" name:", response.name)
print(" display_name:", response.display_name)
print(" state:", response.state)
print(" training... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
DeploymentTraining the above model may take upwards of 240 minutes time.Once your model is done training, you can calculate the actual time it took to train the model by subtracting `end_time` from `start_time`. For your model, you will need to know the fully qualified Vertex Model resource identifier, which the pipel... | while True:
response = get_training_pipeline(pipeline_id, True)
if response.state != aip.PipelineState.PIPELINE_STATE_SUCCEEDED:
print("Training job has not completed:", response.state)
model_to_deploy_id = None
if response.state == aip.PipelineState.PIPELINE_STATE_FAILED:
ra... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Model informationNow that your model is trained, you can get some information on your model. Evaluate the Model resourceNow find out how good the model service believes your model is. As part of training, some portion of the dataset was set aside as the test (holdout) data, which is used by the pipeline service to ev... | def list_model_evaluations(name):
response = clients['model'].list_model_evaluations(parent=name)
for evaluation in response:
print("model_evaluation")
print(" name:", evaluation.name)
print(" metrics_schema_uri:", evaluation.metrics_schema_uri)
metrics = json_format.MessageToDic... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Model deployment for batch predictionNow deploy the trained Vertex `Model` resource you created for batch prediction. This differs from deploying a `Model` resource for on-demand prediction.For online prediction, you:1. Create an `Endpoint` resource for deploying the `Model` resource to.2. Deploy the `Model` resource ... | import json
import_file = IMPORT_FILES[0]
test_items = ! gsutil cat $import_file | head -n2
cols = str(test_items[0]).split(',')
test_item_1 = str(cols[0])
test_label_1 = str(cols[-1])
cols = str(test_items[1]).split(',')
test_item_2 = str(cols[0])
test_label_2 = str(cols[-1])
print(test_item_1, test_label_1)
print... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Make a batch input fileNow make a batch input file, which you store in your local Cloud Storage bucket. The batch input file can be either CSV or JSONL. You will use JSONL in this tutorial. For JSONL file, you make one dictionary entry per line for each video. The dictionary contains the key/value pairs:- `content`: T... | import json
import tensorflow as tf
gcs_input_uri = BUCKET_NAME + '/test.jsonl'
with tf.io.gfile.GFile(gcs_input_uri, 'w') as f:
data = { "content": test_item_1, "mimeType": "video/avi", "timeSegmentStart": "0.0s", 'timeSegmentEnd': '5.0s' }
f.write(json.dumps(data) + '\n')
data = { "content": test_item_2... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Compute instance scalingYou have several choices on scaling the compute instances for handling your batch prediction requests:- Single Instance: The batch prediction requests are processed on a single compute instance. - Set the minimum (`MIN_NODES`) and maximum (`MAX_NODES`) number of compute instances to one.- Manu... | MIN_NODES = 1
MAX_NODES = 1 | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Make batch prediction requestNow that your batch of two test items is ready, let's do the batch request. Use this helper function `create_batch_prediction_job`, with the following parameters:- `display_name`: The human readable name for the prediction job.- `model_name`: The Vertex fully qualified identifier for the `... | BATCH_MODEL = "golf_batch-" + TIMESTAMP
def create_batch_prediction_job(display_name, model_name, gcs_source_uri, gcs_destination_output_uri_prefix, parameters=None):
if DEPLOY_GPU:
machine_spec = {
"machine_type": DEPLOY_COMPUTE,
"accelerator_type": DEPLOY_GPU,
"accel... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Now get the unique identifier for the batch prediction job you created. | # The full unique ID for the batch job
batch_job_id = response.name
# The short numeric ID for the batch job
batch_job_short_id = batch_job_id.split('/')[-1]
print(batch_job_id) | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Get information on a batch prediction jobUse this helper function `get_batch_prediction_job`, with the following paramter:- `job_name`: The Vertex fully qualified identifier for the batch prediction job.The helper function calls the job client service's `get_batch_prediction_job` method, with the following paramter:- ... | def get_batch_prediction_job(job_name, silent=False):
response = clients['job'].get_batch_prediction_job(name=job_name)
if silent:
return response.output_config.gcs_destination.output_uri_prefix, response.state
print("response")
print(" name:", response.name)
print(" display_name:", respons... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Get the predictionsWhen the batch prediction is done processing, the job state will be `JOB_STATE_SUCCEEDED`.Finally you view the predictions stored at the Cloud Storage path you set as output. The predictions will be in a JSONL format, which you indicated at the time you made the batch prediction job, under a subfold... | def get_latest_predictions(gcs_out_dir):
''' Get the latest prediction subfolder using the timestamp in the subfolder name'''
folders = !gsutil ls $gcs_out_dir
latest = ""
for folder in folders:
subfolder = folder.split('/')[-2]
if subfolder.startswith('prediction-'):
if subf... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Cleaning upTo clean up all GCP resources used in this project, you can [delete the GCPproject](https://cloud.google.com/resource-manager/docs/creating-managing-projectsshutting_down_projects) you used for the tutorial.Otherwise, you can delete the individual resources you created in this tutorial:- Dataset- Pipeline- ... | delete_dataset = True
delete_pipeline = True
delete_model = True
delete_endpoint = True
delete_batchjob = True
delete_customjob = True
delete_hptjob = True
delete_bucket = True
# Delete the dataset using the Vertex fully qualified identifier for the dataset
try:
if delete_dataset and 'dataset_id' in globals():
... | _____no_output_____ | Apache-2.0 | notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb | shenzhimo2/vertex-ai-samples |
Tensor analysis using Amazon SageMaker DebuggerLooking at the distributions of activation inputs/outputs, gradients and weights per layer can give useful insights. For instance, it helps to understand whether the model runs into problems like neuron saturation, whether there are layers in your model that are not learn... | ! python -m pip install smdebug | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Configuring the inputs for the training jobNow we'll call the Sagemaker MXNet Estimator to kick off a training job . The `entry_point_script` points to the MXNet training script. The users can create a custom *SessionHook* in their training script. If they chose not to create such hook in the training script (similar ... | entry_point_script = 'mnist.py'
bad_hyperparameters = {'initializer': 2, 'lr': 0.001}
import sagemaker
from sagemaker.mxnet import MXNet
from sagemaker.debugger import DebuggerHookConfig, CollectionConfig
import boto3
import os
estimator = MXNet(role=sagemaker.get_execution_role(),
base_job_name='mxn... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Start the training job | estimator.fit(wait=False) | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Get S3 location of tensorsWe can get information related to the training job: | job_name = estimator.latest_training_job.name
client = estimator.sagemaker_session.sagemaker_client
description = client.describe_training_job(TrainingJobName=job_name)
description | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
We can retrieve the S3 location of the tensors: | path = estimator.latest_job_debugger_artifacts_path()
print('Tensors are stored in: ', path) | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
We can check the status of our training job, by executing `describe_training_job`: | job_name = estimator.latest_training_job.name
print('Training job name: {}'.format(job_name))
client = estimator.sagemaker_session.sagemaker_client
description = client.describe_training_job(TrainingJobName=job_name) | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
We can access the tensors from S3 once the training job is in status `Training` or `Completed`. In the following code cell we check the job status: | import time
if description['TrainingJobStatus'] != 'Completed':
while description['SecondaryStatus'] not in {'Training', 'Completed'}:
description = client.describe_training_job(TrainingJobName=job_name)
primary_status = description['TrainingJobStatus']
secondary_status = description['Secon... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Once the job is in status `Training` or `Completed`, we can create the trial that allows us to access the tensors in Amazon S3. | from smdebug.trials import create_trial
trial1 = create_trial(path) | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
We can check the available steps. A step presents one forward and backward pass. | trial1.steps() | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
As training progresses more steps will become available. Next we will access specific tensors like weights, gradients and activation outputs and plot their distributions. We will use Amazon SageMaker Debugger and define custom rules to retrieve certain tensors. Rules are supposed to return True or False. However in th... | from smdebug.trials import create_trial
from smdebug.rules.rule_invoker import invoke_rule
from smdebug.exceptions import NoMoreData
from smdebug.rules.rule import Rule
import numpy as np
import utils
import collections
import os
from IPython.display import Image
class ActivationOutputs(Rule):
def __init__(self, ba... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Plot the histograms | utils.create_interactive_matplotlib_histogram(rule.tensors, filename='images/activation_outputs.gif')
Image(url='images/activation_outputs.gif') | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Activation InputsIn this rule we look at the inputs into activation function, rather than the output. This can be helpful to understand if there are extreme negative or positive values that saturate the activation functions. | class ActivationInputs(Rule):
def __init__(self, base_trial):
super().__init__(base_trial)
self.tensors = collections.OrderedDict()
def invoke_at_step(self, step):
for tname in self.base_trial.tensor_names(regex='.*relu_input'):
if "gradients" not in tname:
... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Plot the histograms | utils.create_interactive_matplotlib_histogram(rule.tensors, filename='images/activation_inputs.gif') | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
We can see that second convolutional layer `conv1_relu_input_0` receives only negative input values, which means that all ReLUs in this layer output 0. | Image(url='images/activation_inputs.gif') | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
GradientsThe following code retrieves the gradients and plots their distribution. If variance is tiny, that means that the model parameters do not get updated effectively with each training step or that the training has converged to a minimum. | class GradientsLayer(Rule):
def __init__(self, base_trial):
super().__init__(base_trial)
self.tensors = collections.OrderedDict()
def invoke_at_step(self, step):
for tname in self.base_trial.tensor_names(regex='.*gradient'):
try:
tensor = self.bas... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Plot the histograms | utils.create_interactive_matplotlib_histogram(rule.tensors, filename='images/gradients.gif')
Image(url='images/gradients.gif') | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Check variance across layersThe rule retrieves gradients, but this time we compare variance of gradient distribution across layers. We want to identify if there is a large difference between the min and max variance per training step. For instance, very deep neural networks may suffer from vanishing gradients the deep... | class GradientsAcrossLayers(Rule):
def __init__(self, base_trial, ):
super().__init__(base_trial)
self.tensors = collections.OrderedDict()
def invoke_at_step(self, step):
for tname in self.base_trial.tensor_names(regex='.*gradient'):
try:
tensor =... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Let's check min and max values of the gradients across layers: | for step in rule.tensors:
print("Step", step, "variance of gradients: ", rule.tensors[step][0], " to ", rule.tensors[step][1]) | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Distribution of weightsThis rule retrieves the weight tensors and checks the variance. If the distribution does not change much across steps it may indicate that the learning rate is too low, that gradients are too small or that the training has converged to a minimum. | class WeightRatio(Rule):
def __init__(self, base_trial, ):
super().__init__(base_trial)
self.tensors = collections.OrderedDict()
def invoke_at_step(self, step):
for tname in self.base_trial.tensor_names(regex='.*weight'):
if "gradient" not in tname:
... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Plot the histograms | utils.create_interactive_matplotlib_histogram(rule.tensors, filename='images/weights.gif')
Image(url='images/weights.gif') | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
InputsThis rule retrieves layer inputs excluding activation inputs. | class Inputs(Rule):
def __init__(self, base_trial, ):
super().__init__(base_trial)
self.tensors = collections.OrderedDict()
def invoke_at_step(self, step):
for tname in self.base_trial.tensor_names(regex='.*input'):
if "relu" not in tname:
try:
... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Plot the histograms | utils.create_interactive_matplotlib_histogram(rule.tensors, filename='images/layer_inputs.gif')
Image(url='images/layer_inputs.gif') | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Layer outputsThis rule retrieves outputs of layers excluding activation outputs. | class Outputs(Rule):
def __init__(self, base_trial, ):
super().__init__(base_trial)
self.tensors = collections.OrderedDict()
def invoke_at_step(self, step):
for tname in self.base_trial.tensor_names(regex='.*output'):
if "relu" not in tname:
try:
... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Plot the histograms | utils.create_interactive_matplotlib_histogram(rule.tensors, filename='images/layer_outputs.gif')
Image(url='images/layer_outputs.gif') | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Comparison In the previous section we have looked at the distribution of gradients, activation outputs and weights of a model that has not trained well due to poor initialization. Now we will compare some of these distributions with a model that has been well intialized. | entry_point_script = 'mnist.py'
hyperparameters = {'lr': 0.01}
estimator = MXNet(role=sagemaker.get_execution_role(),
base_job_name='mxnet',
train_instance_count=1,
train_instance_type='ml.m5.xlarge',
train_volume_size=400,
source... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Start the training job | estimator.fit(wait=False) | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Get S3 path where tensors have been stored | path = estimator.latest_job_debugger_artifacts_path()
print('Tensors are stored in: ', path) | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Check the status of the training job: | job_name = estimator.latest_training_job.name
print('Training job name: {}'.format(job_name))
client = estimator.sagemaker_session.sagemaker_client
description = client.describe_training_job(TrainingJobName=job_name)
if description['TrainingJobStatus'] != 'Completed':
while description['SecondaryStatus'] not in ... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Now we create a new trial object `trial2`: | from smdebug.trials import create_trial
trial2 = create_trial(path) | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
GradientsLets compare distribution of gradients of the convolutional layers of both trials. `trial` is the trial object of the first training job, `trial2` is the trial object of second training job. We can now easily compare tensors from both training jobs. | rule = GradientsLayer(trial1)
try:
invoke_rule(rule)
except NoMoreData:
print('The training has ended and there is no more data to be analyzed. This is expected behavior.')
dict_gradients = {}
dict_gradients['gradient/conv0_weight_bad_hyperparameters'] = rule.tensors['gradient/conv0_weight']
dict_gradients['gr... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Second trial: | rule = GradientsLayer(trial2)
try:
invoke_rule(rule)
except NoMoreData:
print('The training has ended and there is no more data to be analyzed. This is expected behavior.')
dict_gradients['gradient/conv0_weight_good_hyperparameters'] = rule.tensors['gradient/conv0_weight']
dict_gradients['gradient/conv1_weight... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Plot the histograms | utils.create_interactive_matplotlib_histogram(dict_gradients, filename='images/gradients_comparison.gif') | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
In the case of the poorly initalized model, gradients are fluctuating a lot leading to very high variance. | Image(url='images/gradients_comparison.gif') | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Activation inputsLets compare distribution of activation inputs of both trials. | rule = ActivationInputs(trial1)
try:
invoke_rule(rule)
except NoMoreData:
print('The training has ended and there is no more data to be analyzed. This is expected behavior.')
dict_activation_inputs = {}
dict_activation_inputs['conv0_relu_input_0_bad_hyperparameters'] = rule.tensors['conv0_relu_input_0']
dict_a... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Second trial | rule = ActivationInputs(trial2)
try:
invoke_rule(rule)
except NoMoreData:
print('The training has ended and there is no more data to be analyzed. This is expected behavior.')
dict_activation_inputs['conv0_relu_input_0_good_hyperparameters'] = rule.tensors['conv0_relu_input_0']
dict_activation_inputs['conv1_rel... | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Plot the histograms | utils.create_interactive_matplotlib_histogram(dict_activation_inputs, filename='images/activation_inputs_comparison.gif') | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
The distribution of activation inputs into first activation layer `conv0_relu_input_0` look quite similar in both trials. However in the case of the second layer they drastically differ. | Image(url='images/activation_inputs_comparison.gif') | _____no_output_____ | Apache-2.0 | sagemaker-debugger/mnist_tensor_analysis/mnist_tensor_analysis.ipynb | P15241328/amazon-sagemaker-examples |
Copyright 2019 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/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
単語埋め込み (Word embeddings) View on TensorFlow.org Run in Google Colab View source on GitHub Download notebook Note: これらのドキュメントは私たちTensorFlowコミュニティが翻訳したものです。コミュニティによる 翻訳は**ベストエフォート**であるため、この翻訳が正確であることや[英語の公式ドキュメント](https://www.tensorflow.org/?hl=en)の 最新の状態を反映したものであることを... | from __future__ import absolute_import, division, print_function, unicode_literals
try:
# %tensorflow_version は Colab 中でのみ使用できます
!pip install tf-nightly
except Exception:
pass
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_datasets as tfds
tfds.disable... | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
Embedding レイヤーを使うKeras では単語埋め込みを使うのも簡単です。[Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding) レイヤーを見てみましょう。 Embedding レイヤーは、（特定の単語を示す）整数のインデックスに（その埋め込みである）密なベクトルを対応させる参照テーブルとして理解することができます。埋め込みの次元数（あるいはその幅）は、取り組んでいる問題に適した値を実験して求めるパラメータです。これは、Dense レイヤーの中のニューロンの数を実験で求めるのとまったくおなじです。 | embedding_layer = layers.Embedding(1000, 5) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
Embedding レイヤーを作成するとき、埋め込みの重みは（ほかのレイヤーとおなじように）ランダムに初期化されます。訓練を通じて、これらの重みはバックプロパゲーションによって徐々に調整されます。いったん訓練が行われると、学習された単語埋め込みは、（モデルを訓練した特定の問題のために学習された結果）単語の間の類似性をおおまかにコード化しています。Embedding レイヤーに整数を渡すと、結果はそれぞれの整数が埋め込みテーブルのベクトルに置き換えられます。 | result = embedding_layer(tf.constant([1,2,3]))
result.numpy() | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
テキストあるいはシーケンスの問題では、入力として、Embedding レイヤーは shape が `(samples, sequence_length)` の2次元整数テンソルを取ります。ここで、各エントリは整数のシーケンスです。このレイヤーは、可変長のシーケンスを埋め込みベクトルにすることができます。上記のバッチでは、 `(32, 10)` （長さ10のシーケンス32個のバッチ）や、 `(64, 15)` （長さ15のシーケンス64個のバッチ）を埋め込みレイヤーに投入可能です。返されたテンソルは入力より 1つ軸が多くなっており、埋め込みベクトルはその最後の新しい軸に沿って並べられます。`(2, 3)` の入力バッチを渡すと、出力は... | result = embedding_layer(tf.constant([[0,1,2],[3,4,5]]))
result.shape | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
シーケンスのバッチを入力されると、Embedding レイヤーは shape が `(samples, sequence_length, embedding_dimensionality)` の3次元浮動小数点数テンソルを返します。この可変長のシーケンスを、固定長の表現に変換するには、さまざまな標準的なアプローチが存在します。Dense レイヤーに渡す前に、RNNやアテンション、プーリングレイヤーを使うことができます。ここでは、一番単純なのでプーリングを使用します。[RNN を使ったテキスト分類](https://github.com/tensorflow/docs/blob/master/site/ja/tutorials/tex... | (train_data, test_data), info = tfds.load(
'imdb_reviews/subwords8k',
split = (tfds.Split.TRAIN, tfds.Split.TEST),
with_info=True, as_supervised=True) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
エンコーダー（`tfds.features.text.SubwordTextEncoder`）を取得し、すこしボキャブラリを見てみましょう。ボキャブラリ中の "\_" は空白を表しています。ボキャブラリの中にどんなふうに（"\_"）で終わる単語全体と、長い単語を構成する単語の一部が含まれているかに注目してください。 | encoder = info.features['text'].encoder
encoder.subwords[:20] | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
映画のレビューはそれぞれ長さが異なっているはずです。`padded_batch` メソッドを使ってレビューの長さを標準化します。 | train_batches = train_data.shuffle(1000).padded_batch(10)
test_batches = test_data.shuffle(1000).padded_batch(10) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
インポートした状態では、レビューのテキストは整数エンコードされています（それぞれの整数がボキャブラリ中の特定の単語あるいは部分単語を表しています）。あとの方のゼロに注目してください。これは、バッチが一番長いサンプルに合わせてパディングされた結果です。 | train_batch, train_labels = next(iter(train_batches))
train_batch.numpy() | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
単純なモデルの構築[Keras Sequential API](../../guide/keras) を使ってモデルを定義することにします。今回の場合、モデルは「連続した Bag of Words」スタイルのモデルです。* 次のレイヤーは Embedding レイヤーです。このレイヤーは整数エンコードされた語彙を受け取り、それぞれの単語のインデックスに対応する埋め込みベクトルをみつけて取り出します。これらのベクトルはモデルの訓練により学習されます。このベクトルは出力配列に次元を追加します。その結果次元は `(batch, sequence, embedding)` となります。* 次に、GlobalAveragePooling1D... | embedding_dim=16
model = keras.Sequential([
layers.Embedding(encoder.vocab_size, embedding_dim),
layers.Dense(16, activation='relu'),
layers.GlobalAveragePooling1D(),
layers.Dense(1, activation='sigmoid')
])
model.summary() | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
モデルのコンパイルと訓練 | model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
history = model.fit(
train_batches,
epochs=10,
validation_data=test_batches, validation_steps=20) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
このアプローチにより、モデルの評価時の正解率は 88% 前後に達します（モデルは過学習しており、訓練時の正解率の方が際立って高いことに注意してください）。 | import matplotlib.pyplot as plt
history_dict = history.history
acc = history_dict['accuracy']
val_acc = history_dict['val_accuracy']
loss = history_dict['loss']
val_loss = history_dict['val_loss']
epochs = range(1, len(acc) + 1)
plt.figure(figsize=(12,9))
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot... | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
学習した埋め込みの取得次に、訓練によって学習された単語埋め込みを取得してみます。これは、shape が `(vocab_size, embedding-dimension)` の行列になります。 | e = model.layers[0]
weights = e.get_weights()[0]
print(weights.shape) # shape: (vocab_size, embedding_dim) | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
この重みをディスクに出力します。[Embedding Projector](http://projector.tensorflow.org) を使うため、タブ区切り形式の2つのファイルをアップロードします。（埋め込みを含む）ベクトルのファイルと、（単語を含む）メタデータファイルです。 | import io
encoder = info.features['text'].encoder
out_v = io.open('vecs.tsv', 'w', encoding='utf-8')
out_m = io.open('meta.tsv', 'w', encoding='utf-8')
for num, word in enumerate(encoder.subwords):
vec = weights[num+1] # 0 はパディングのためスキップ
out_m.write(word + "\n")
out_v.write('\t'.join([str(x) for x in vec]) + "\... | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
このチュートリアルを [Colaboratory](https://colab.research.google.com) で実行している場合には、下記のコードを使ってこれらのファイルをローカルマシンにダウンロードすることができます（あるいは、ファイルブラウザを使います。*表示 -> 目次 -> ファイル* ）。 | try:
from google.colab import files
except ImportError:
pass
else:
files.download('vecs.tsv')
files.download('meta.tsv') | _____no_output_____ | Apache-2.0 | site/ja/tutorials/text/word_embeddings.ipynb | mulka/docs |
Методы обучения без учителя Метод главных компонент Внимание! Решение данной задачи предполагает, что у вас установлены библиотека numpy версии 1.16.4 и выше и библиотека scikit-learn версии 0.21.2 и выше. В следующей ячейке мы проверим это. Если у вас установлены более старые версии, обновите их пожалуйста, или во... | import numpy as np
import sklearn | _____no_output_____ | Apache-2.0 | pca/pca.ipynb | myusernameisuseless/python_for_data_analysis_mailru_mipt |
В этом задании мы применим метод главных компонент на многомерных данных и постараемся найти оптимальную размерность признаков для решения задачи классификации | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline | _____no_output_____ | Apache-2.0 | pca/pca.ipynb | myusernameisuseless/python_for_data_analysis_mailru_mipt |
Подготовка данных Исходными [данными](http://archive.ics.uci.edu/ml/machine-learning-databases/auslan2-mld/auslan.data.html) являются показания различных сенсоров, установленных на руках человека, который умеет общаться на языке жестов.В данном случае задача ставится следующим образом: по показаниям датчиков (по 11 се... | # Загружаем данные сенсоров
df_database = pd.read_csv('sign_database.csv')
# Загружаем метки классов
sign_classes = pd.read_csv('sign_classes.csv', index_col=0, header=0, names=['id', 'class'])
# Столбец id - идентификаторы "слов"
# Столбец time - метка времени
# Остальные столбцы - показания серсоров для слова id в м... | _____no_output_____ | Apache-2.0 | pca/pca.ipynb | myusernameisuseless/python_for_data_analysis_mailru_mipt |
Для каждого из "слов" у нас есть набор показаний сенсоров с разных частей руки в каждый момент времени.Идея нашего подхода будет заключаться в следующем – давайте для каждого сенсора составим набор характеристик (например, разброс значений, максимальное, минимальное, среднее значение, количество "пиков", и т.п.) и буде... | ## Если не хотите долго ждать - не убирайте комментарии
# from tsfresh.feature_extraction import extract_features
# from tsfresh.feature_selection import select_features
# from tsfresh.utilities.dataframe_functions import impute
# from tsfresh.feature_extraction import ComprehensiveFCParameters, MinimalFCParameters, se... | _____no_output_____ | Apache-2.0 | pca/pca.ipynb | myusernameisuseless/python_for_data_analysis_mailru_mipt |
Базовая модель В результате у нас получилось очень много признаков (аж 10865), давайте применим метод главных компонент, чтобы получить сжатое признаковое представление, сохранив при этом предиктивную силу в модели. | from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
from sklearn.neighbors import KNeighborsClassifier
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelE... | _____no_output_____ | Apache-2.0 | pca/pca.ipynb | myusernameisuseless/python_for_data_analysis_mailru_mipt |
Создадим бейзлайн без уменьшения размерности. Гиперпараметры модели подбирались произвольно | # Подготовим данные на вход в модель
# признаки
X = sign_features_filtered.values
# классы
enc = LabelEncoder()
enc.fit(sign_classes.loc[:, 'class'])
sign_classes.loc[:, 'target'] = enc.transform(sign_classes.loc[:, 'class'])
y = sign_classes.target.values
# Будем делать кросс-валидацию на 5 фолдов
cv = StratifiedKFo... | _____no_output_____ | Apache-2.0 | pca/pca.ipynb | myusernameisuseless/python_for_data_analysis_mailru_mipt |
Качество базовой модели должно быть в районе 92 процентов. Метод главных компонент * Добавьте в пайплайн `base_model` шаг с методом главных компонент. Начиная с версии 0.18 в sklearn добавили разные солверы для PCA. Дополнитенльно задайте в модели следующие параметры: `svd_solder = "randomized"` и `random_state=123`.*... | numbers
numbers = [i for i in range(9, 19)]
scores = []
for n in numbers:
base_model1 = Pipeline([
('scaler', StandardScaler()),
('pca', PCA(n_components=n, svd_solver='randomized', random_state=123)),
('clf', KNeighborsClassifier(n_neighbors=9))
])
scores.append(cross_val_score(bas... | _____no_output_____ | Apache-2.0 | pca/pca.ipynb | myusernameisuseless/python_for_data_analysis_mailru_mipt |
Ответ | print('{:.2f}'.format(expl)) | 0.39
| Apache-2.0 | pca/pca.ipynb | myusernameisuseless/python_for_data_analysis_mailru_mipt |
Load libraries | !pip install -q -r requirements.txt
import sys
import os
import numpy as np
import pandas as pd
from PIL import Image
import torch
import torch.nn as nn
import torch.utils.data as D
from torch.optim.lr_scheduler import ExponentialLR
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision... | _____no_output_____ | Apache-2.0 | my_notebooks/eval10_experiment5.ipynb | MichelML/ml-aging |
Define dataset and model | img_dir = '../input/rxrxairgb512'
path_data = '../input/rxrxaicsv'
device = 'cuda'
batch_size = 32
torch.manual_seed(0)
model_name = 'efficientnet-b3'
jitter = (0.6, 1.4)
class ImagesDS(D.Dataset):
# taken textbook from https://arxiv.org/pdf/1812.01187.pdf
transform_train = transforms.Compose([
transfor... | Loaded pretrained weights for efficientnet-b3
| Apache-2.0 | my_notebooks/eval10_experiment5.ipynb | MichelML/ml-aging |
Evaluate | model.cuda()
eval_model_10(model, tloader, 'models/Model_efficientnet-b3_93.pth', path_data) | _____no_output_____ | Apache-2.0 | my_notebooks/eval10_experiment5.ipynb | MichelML/ml-aging |
Introduction to the Research EnvironmentThe research environment is powered by IPython notebooks, which allow one to perform a great deal of data analysis and statistical validation. We'll demonstrate a few simple techniques here. Code Cells vs. Text CellsAs you can see, each cell can be either code or text. To select ... | 2 + 2 | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Sometimes there is no result to be printed, as is the case with assignment. | X = 2 | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Remember that only the result from the last line is printed. | 2 + 2
3 + 3 | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
However, you can print whichever lines you want using the `print` statement. | print(2 + 2)
3 + 3 | 4
| MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Knowing When a Cell is RunningWhile a cell is running, a `[*]` will display on the left. When a cell has yet to be executed, `[ ]` will display. When it has been run, a number will display indicating the order in which it was run during the execution of the notebook `[5]`. Try on this cell and note it happening. | #Take some time to run something
c = 0
for i in range(10000000+1):
c = c + i
print(c) | 50000005000000
| MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Ejemplo 1: Progresión Aritmética de Diferencia 1$\frac{n\cdot \left(n+1\right)}{2}=1+2+3+4+5+6+\cdot \cdot \cdot +n$ | n = 10000000
print(int(n*(n+1)/2)) | 50000005000000
| MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Importing LibrariesThe vast majority of the time, you'll want to use functions from pre-built libraries. You can't import every library on Quantopian due to security issues, but you can import most of the common scientific ones. Here I import numpy and pandas, the two most common and useful libraries in quant finance. ... | import numpy as np
import pandas as pd
# This is a plotting library for pretty pictures.
import matplotlib.pyplot as plt | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Tab AutocompletePressing tab will give you a list of IPython's best guesses for what you might want to type next. This is incredibly valuable and will save you a lot of time. If there is only one possible option for what you could type next, IPython will fill that in for you. Try pressing tab very frequently, it will s... | np.random | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Getting Documentation HelpPlacing a question mark after a function and executing that line of code will give you the documentation IPython has for that function. It's often best to do this in a new cell, as you avoid re-executing other code and running into bugs. | np.random.normal? | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Ejemplo 2 Obtener un numero primo entre 1 y 100 | def is_prime(number):
if number <= 1:
return False
elif number <= 3:
return True
if number%2==0 or number%3==0:
return False
i = 5
while i*i <= number:
if number % i == 0 or number % (i+2) == 0:
return False;
return True
n = 0
while True:
n = np.ran... | 49 Es un numero primo
| MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
SamplingWe'll sample some random data using a function from `numpy`. | # Sample 100 points with a mean of 0 and an std of 1. This is a standard normal distribution.
X = np.random.normal(0, 1, 100)
X | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
PlottingWe can use the plotting library we imported as follows. | plt.plot(X) | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Squelching Line OutputYou might have noticed the annoying line of the form `[]` before the plots. This is because the `.plot` function actually produces output. Sometimes we wish not to display output, we can accomplish this with the semi-colon as follows. | plt.plot(X); | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
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