def testRNN(self):
random.seed(42)
data = np.array(list([[2, 1, 2, 2, 3], [2, 2, 3, 4, 5],
[3, 3, 1, 2, 1], [2, 4, 5, 4, 1]]),
dtype=np.float32)
# labels for classification
labels = np.array(list([1, 0, 1, 0]), dtype=np.float32)
# targets for regression
targets = np.array(list([10, 16, 10, 16]), dtype=np.float32)
test_data = np.array(list([[1, 3, 3, 2, 1], [2, 3, 4, 5, 6]]),
dtype=np.float32)
def _input_fn(X):
# pylint: disable=invalid-name
return tf.split(1, 5, X)
# Classification
classifier = learn.TensorFlowRNNClassifier(rnn_size=2,
cell_type="lstm",
n_classes=2,
input_op_fn=_input_fn)
classifier.fit(data, labels)
# pylint: disable=pointless-statement
classifier.weights_
classifier.bias_
# pylint: enable=pointless-statement
predictions = classifier.predict(test_data)
self.assertAllClose(predictions, np.array([1, 0]))
classifier = learn.TensorFlowRNNClassifier(rnn_size=2,
cell_type="rnn",
n_classes=2,
input_op_fn=_input_fn,
num_layers=2)
classifier.fit(data, labels)
classifier = learn.TensorFlowRNNClassifier(
rnn_size=2,
cell_type="invalid_cell_type",
n_classes=2,
input_op_fn=_input_fn,
num_layers=2)
with self.assertRaises(ValueError):
classifier.fit(data, labels)
# Regression
regressor = learn.TensorFlowRNNRegressor(rnn_size=2,
cell_type="gru",
input_op_fn=_input_fn)
regressor.fit(data, targets)
# pylint: disable=pointless-statement
regressor.weights_
regressor.bias_
# pylint: enable=pointless-statement
predictions = regressor.predict(test_data)
def testRNN(self):
random.seed(42)
import numpy as np
data = np.array(list([[2, 1, 2, 2, 3], [2, 2, 3, 4, 5],
[3, 3, 1, 2, 1], [2, 4, 5, 4, 1]]),
dtype=np.float32)
# labels for classification
labels = np.array(list([1, 0, 1, 0]), dtype=np.float32)
# targets for regression
targets = np.array(list([10, 16, 10, 16]), dtype=np.float32)
test_data = np.array(list([[1, 3, 3, 2, 1], [2, 3, 4, 5, 6]]))
def input_fn(X):
return tf.split(1, 5, X)
# Classification
classifier = learn.TensorFlowRNNClassifier(rnn_size=2,
cell_type='lstm',
n_classes=2,
input_op_fn=input_fn)
classifier.fit(data, labels)
classifier.weights_
classifier.bias_
predictions = classifier.predict(test_data)
self.assertAllClose(predictions, np.array([1, 0]))
classifier = learn.TensorFlowRNNClassifier(rnn_size=2,
cell_type='rnn',
n_classes=2,
input_op_fn=input_fn,
num_layers=2)
classifier.fit(data, labels)
classifier = learn.TensorFlowRNNClassifier(
rnn_size=2,
cell_type='invalid_cell_type',
n_classes=2,
input_op_fn=input_fn,
num_layers=2)
with self.assertRaises(ValueError):
classifier.fit(data, labels)
# Regression
regressor = learn.TensorFlowRNNRegressor(rnn_size=2,
cell_type='gru',
input_op_fn=input_fn)
regressor.fit(data, targets)
regressor.weights_
regressor.bias_
predictions = regressor.predict(test_data)
def testBidirectionalRNN(self):
random.seed(42)
import numpy as np
data = np.array(list([[2, 1, 2, 2, 3], [2, 2, 3, 4, 5],
[3, 3, 1, 2, 1], [2, 4, 5, 4, 1]]),
dtype=np.float32)
labels = np.array(list([1, 0, 1, 0]), dtype=np.float32)
def input_fn(X):
return tf.split(1, 5, X)
# Classification
classifier = learn.TensorFlowRNNClassifier(rnn_size=2,
cell_type='lstm',
n_classes=2,
input_op_fn=input_fn,
bidirectional=True)
classifier.fit(data, labels)
predictions = classifier.predict(
np.array(list([[1, 3, 3, 2, 1], [2, 3, 4, 5, 6]])))
self.assertAllClose(predictions, np.array([1, 0]))
def testBidirectionalRNN(self):
random.seed(42)
data = np.array(list([[2, 1, 2, 2, 3], [2, 2, 3, 4, 5],
[3, 3, 1, 2, 1], [2, 4, 5, 4, 1]]),
dtype=np.float32)
labels = np.array(list([1, 0, 1, 0]), dtype=np.float32)
def _input_fn(X):
# pylint: disable=invalid-name
return tf.split(1, 5, X)
# Classification
classifier = learn.TensorFlowRNNClassifier(rnn_size=2,
cell_type="lstm",
n_classes=2,
input_op_fn=_input_fn,
bidirectional=True)
classifier.fit(data, labels)
test_data = np.array(list([[1, 3, 3, 2, 1], [2, 3, 4, 5, 6]]),
dtype=np.float32)
predictions = classifier.predict(test_data)
self.assertAllClose(predictions, np.array([1, 0]))