Revision e1467a79dc6580ae009d827b5e6f274faff3b339 authored by liqunfu on 27 March 2020, 21:42:04 UTC, committed by GitHub on 27 March 2020, 21:42:04 UTC
support Pooling ops with Sequence axis
LogisticRegression_GraphAPI.py
# ==============================================================================
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE.md file in the project root
# for full license information.
# ==============================================================================
# This example shows how to train a very basic CNTK model for logistic regression.
# The task is to classify a 2-dimensional vector as belonging to one of two classes.
# The data is artificially created.
# This example is identical to LogisticRegression_FunctionalAPI.py, except that
# it uses lower-level APIs.
from __future__ import print_function
import cntk
import numpy as np
import scipy.sparse
# Define the task.
input_dim = 2 # classify 2-dimensional data
num_classes = 2 # into one of two classes
# This example uses synthetic data from a normal distribution, which we generate in the following.
# X[corpus_size,input_dim] - our input data
# Y[corpus_size] - labels (0 or 1), in one-hot representation
np.random.seed(0)
def generate_synthetic_data(N):
Y = np.random.randint(size=N, low=0, high=num_classes) # labels
X = (np.random.randn(N, input_dim)+3) * (Y[:,None]+1) # data
# Our model expects float32 features, and cross-entropy expects one-hot encoded labels.
Y = scipy.sparse.csr_matrix((np.ones(N,np.float32), (range(N), Y)), shape=(N, num_classes))
X = X.astype(np.float32)
return X, Y
X_train, Y_train = generate_synthetic_data(20000)
X_test, Y_test = generate_synthetic_data(1024)
# Define the CNTK model function. The model function maps input data to
# predictions (here: 2-dimensional inputs --> 2 scores).
# This simple logistic-regression model just uses a linear transform.
data = cntk.input_variable(input_dim)
W = cntk.Parameter((input_dim, num_classes), init=cntk.glorot_uniform(), name='W')
b = cntk.Parameter((num_classes,), init=0, name='b')
model = cntk.times(data, W) + b
# Define the CNTK criterion function. A criterion function maps
# (input vectors, labels) to a loss function and an optional additional
# metric. The loss function is used to train the model parameters.
# We use cross entropy as a loss function.
label_one_hot = cntk.input_variable(num_classes, is_sparse=True)
loss = cntk.cross_entropy_with_softmax(model, label_one_hot) # this applies softmax to model's output under the hood
metric = cntk.classification_error(model, label_one_hot)
criterion = cntk.combine([loss, metric]) # criterion is a tuple-valued function (loss, metric)
# Learner object. The learner implements the update algorithm, in this case plain SGD.
learning_rate = 0.1
learner = cntk.sgd(model.parameters, cntk.learning_parameter_schedule(learning_rate))
# Trainer.
minibatch_size = 32
progress_writer = cntk.logging.ProgressPrinter(50) # helper for logging progress; log every 50 minibatches
trainer = cntk.Trainer(None, criterion, [learner], [progress_writer])
# Train!
for i in range(0, len(X_train), minibatch_size): # loop over minibatches
x = X_train[i:i+minibatch_size] # get one minibatch worth of data
y = Y_train[i:i+minibatch_size]
trainer.train_minibatch({data: x, label_one_hot: y}) # update model from one minibatch
trainer.summarize_training_progress()
# Test error rate on the test set.
evaluator = cntk.Evaluator(metric, [progress_writer])
for i in range(0, len(X_test), minibatch_size): # loop over minibatches
x = X_test[i:i+minibatch_size] # get one minibatch worth of data
y = Y_test[i:i+minibatch_size]
evaluator.test_minibatch({data: x, label_one_hot: y}) # test one minibatch
evaluator.summarize_test_progress()
# Inspect predictions on one minibatch, for illustration.
# For evaluation, we map the output of the network between 0-1 and convert them into probabilities
# for the two classes. We use a softmax function to get the probabilities of each of the class.
get_probability = cntk.softmax(model)
X_check, Y_check = generate_synthetic_data(25) # a small batch of 25 examples
result = get_probability.eval(X_check)
print("Label :", [label.todense().argmax() for label in Y_check])
print("Predicted:", [result[i,:].argmax() for i in range(len(result))])
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