from __future__ import print_function
import numpy as np
np.random.seed(1337)
from keras.utils.test_utils import get_test_data
from keras.models import Sequential
from keras.layers.core import Dense, Activation, TimeDistributedDense, Flatten
from keras.layers.recurrent import GRU
from keras.layers.convolutional import Convolution2D
from keras.utils.np_utils import to_categorical
import unittest
class TestRegularizers(unittest.TestCase):
def test_vector_clf(self):
nb_hidden = 10
print('vector classification data:')
(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(10,),
classification=True, nb_class=2)
print('X_train:', X_train.shape)
print('X_test:', X_test.shape)
print('y_train:', y_train.shape)
print('y_test:', y_test.shape)
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
model = Sequential()
model.add(Dense(X_train.shape[-1], nb_hidden))
model.add(Activation('relu'))
model.add(Dense(nb_hidden, y_train.shape[-1]))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2)
print(history.history)
self.assertTrue(history.history['val_acc'][-1] > 0.9)
def test_vector_reg(self):
nb_hidden = 10
print('vector regression data:')
(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(10,), output_shape=(2,),
classification=False)
print('X_train:', X_train.shape)
print('X_test:', X_test.shape)
print('y_train:', y_train.shape)
print('y_test:', y_test.shape)
model = Sequential()
model.add(Dense(X_train.shape[-1], nb_hidden))
model.add(Activation('tanh'))
model.add(Dense(nb_hidden, y_train.shape[-1]))
model.compile(loss='hinge', optimizer='adagrad')
history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), verbose=2)
self.assertTrue(history.history['val_loss'][-1] < 0.9)
def test_temporal_clf(self):
print('temporal classification data:')
(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(5,10),
classification=True, nb_class=2)
print('X_train:', X_train.shape)
print('X_test:', X_test.shape)
print('y_train:', y_train.shape)
print('y_test:', y_test.shape)
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
model = Sequential()
model.add(GRU(X_train.shape[-1], y_train.shape[-1]))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adadelta')
history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2)
self.assertTrue(history.history['val_acc'][-1] > 0.9)
def test_temporal_reg(self):
print('temporal regression data:')
(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(5, 10), output_shape=(2,),
classification=False)
print('X_train:', X_train.shape)
print('X_test:', X_test.shape)
print('y_train:', y_train.shape)
print('y_test:', y_test.shape)
model = Sequential()
model.add(GRU(X_train.shape[-1], y_train.shape[-1]))
model.compile(loss='hinge', optimizer='adam')
history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), verbose=2)
self.assertTrue(history.history['val_loss'][-1] < 0.8)
def test_seq_to_seq(self):
print('sequence to sequence data:')
(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(5, 10), output_shape=(5, 10),
classification=False)
print('X_train:', X_train.shape)
print('X_test:', X_test.shape)
print('y_train:', y_train.shape)
print('y_test:', y_test.shape)
model = Sequential()
model.add(TimeDistributedDense(X_train.shape[-1], y_train.shape[-1]))
model.compile(loss='hinge', optimizer='rmsprop')
history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), verbose=2)
self.assertTrue(history.history['val_loss'][-1] < 0.75)
def test_img_clf(self):
print('image classification data:')
(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(3, 32, 32),
classification=True, nb_class=2)
print('X_train:', X_train.shape)
print('X_test:', X_test.shape)
print('y_train:', y_train.shape)
print('y_test:', y_test.shape)
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
model = Sequential()
model.add(Convolution2D(32, 3, 32, 32))
model.add(Activation('sigmoid'))
model.add(Flatten())
model.add(Dense(32, y_test.shape[-1]))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd')
history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2)
self.assertTrue(history.history['val_acc'][-1] > 0.9)
if __name__ == '__main__':
print('Test different types of classification and regression tasks')
unittest.main()
