130 lines
5.6 KiB
Python
130 lines
5.6 KiB
Python
from __future__ import print_function
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import numpy as np
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np.random.seed(1337)
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from keras.utils.test_utils import get_test_data
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from keras.models import Sequential
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from keras.layers.core import Dense, Activation, TimeDistributedDense, Flatten
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from keras.layers.recurrent import GRU
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from keras.layers.convolutional import Convolution2D
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from keras.utils.np_utils import to_categorical
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import unittest
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class TestRegularizers(unittest.TestCase):
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def test_vector_clf(self):
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nb_hidden = 10
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print('vector classification data:')
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(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=1000, input_shape=(10,),
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classification=True, nb_class=2)
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print('X_train:', X_train.shape)
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print('X_test:', X_test.shape)
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print('y_train:', y_train.shape)
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print('y_test:', y_test.shape)
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y_train = to_categorical(y_train)
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y_test = to_categorical(y_test)
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model = Sequential()
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model.add(Dense(X_train.shape[-1], nb_hidden))
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model.add(Activation('relu'))
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model.add(Dense(nb_hidden, y_train.shape[-1]))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
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history = model.fit(X_train, y_train, nb_epoch=10, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2)
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self.assertTrue(history.validation_accuracy[-1] > 0.9)
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def test_vector_reg(self):
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nb_hidden = 10
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print('vector regression data:')
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(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=1000, input_shape=(10,), output_shape=(2,),
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classification=False)
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print('X_train:', X_train.shape)
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print('X_test:', X_test.shape)
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print('y_train:', y_train.shape)
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print('y_test:', y_test.shape)
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model = Sequential()
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model.add(Dense(X_train.shape[-1], nb_hidden))
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model.add(Activation('relu'))
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model.add(Dense(nb_hidden, y_train.shape[-1]))
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model.compile(loss='hinge', optimizer='rmsprop')
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history = model.fit(X_train, y_train, nb_epoch=10, batch_size=16, validation_data=(X_test, y_test), verbose=2)
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self.assertTrue(history.validation_loss[-1] < 0.75)
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def test_temporal_clf(self):
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print('temporal classification data:')
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(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=1000, input_shape=(5,10),
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classification=True, nb_class=2)
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print('X_train:', X_train.shape)
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print('X_test:', X_test.shape)
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print('y_train:', y_train.shape)
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print('y_test:', y_test.shape)
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y_train = to_categorical(y_train)
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y_test = to_categorical(y_test)
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model = Sequential()
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model.add(GRU(X_train.shape[-1], y_train.shape[-1]))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
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history = model.fit(X_train, y_train, nb_epoch=10, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2)
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self.assertTrue(history.validation_accuracy[-1] > 0.9)
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def test_temporal_reg(self):
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print('temporal regression data:')
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(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=1000, input_shape=(5, 10), output_shape=(2,),
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classification=False)
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print('X_train:', X_train.shape)
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print('X_test:', X_test.shape)
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print('y_train:', y_train.shape)
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print('y_test:', y_test.shape)
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model = Sequential()
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model.add(GRU(X_train.shape[-1], y_train.shape[-1]))
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model.compile(loss='hinge', optimizer='rmsprop')
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history = model.fit(X_train, y_train, nb_epoch=10, batch_size=16, validation_data=(X_test, y_test), verbose=2)
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self.assertTrue(history.validation_loss[-1] < 0.75)
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def test_seq_to_seq(self):
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print('sequence to sequence data:')
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(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=1000, input_shape=(5, 10), output_shape=(5, 10),
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classification=False)
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print('X_train:', X_train.shape)
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print('X_test:', X_test.shape)
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print('y_train:', y_train.shape)
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print('y_test:', y_test.shape)
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model = Sequential()
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model.add(TimeDistributedDense(X_train.shape[-1], y_train.shape[-1]))
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model.compile(loss='hinge', optimizer='rmsprop')
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history = model.fit(X_train, y_train, nb_epoch=10, batch_size=16, validation_data=(X_test, y_test), verbose=2)
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self.assertTrue(history.validation_loss[-1] < 0.75)
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def test_img_clf(self):
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print('image classification data:')
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(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=1000, input_shape=(3, 32, 32),
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classification=True, nb_class=2)
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print('X_train:', X_train.shape)
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print('X_test:', X_test.shape)
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print('y_train:', y_train.shape)
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print('y_test:', y_test.shape)
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y_train = to_categorical(y_train)
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y_test = to_categorical(y_test)
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model = Sequential()
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model.add(Convolution2D(32, 3, 32, 32))
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model.add(Activation('relu'))
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model.add(Flatten())
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model.add(Dense(32, y_test.shape[-1]))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
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history = model.fit(X_train, y_train, nb_epoch=10, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2)
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self.assertTrue(history.validation_accuracy[-1] > 0.9)
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if __name__ == '__main__':
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print('Test different types of classification and regression tasks')
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unittest.main() |