def test_siamese_2():
(X_train, y_train), (X_test, y_test) = _get_test_data()
left = Sequential()
left.add(Dense(nb_hidden, input_shape=(input_dim,)))
left.add(Activation('relu'))
right = Sequential()
right.add(Dense(nb_hidden, input_shape=(input_dim,)))
right.add(Activation('relu'))
add_shared_layer(Dense(nb_hidden), [left, right])
left.add(Dense(nb_hidden))
right.add(Dense(nb_hidden))
add_shared_layer(Dense(nb_hidden), [left, right])
model = Sequential()
model.add(Merge([left, right], mode='sum'))
model.add(Dense(nb_class))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=0, validation_data=([X_test, X_test], y_test))
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=0, validation_data=([X_test, X_test], y_test))
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=0, validation_split=0.1)
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=0, validation_split=0.1)
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0)
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0, shuffle=False)
loss = model.evaluate([X_test, X_test], y_test, verbose=0)
assert(loss < 0.8)
model.predict([X_test, X_test], verbose=0)
model.predict_classes([X_test, X_test], verbose=0)
model.predict_proba([X_test, X_test], verbose=0)
model.get_config(verbose=0)
# test weight saving
fname = 'test_siamese_2.h5'
model.save_weights(fname, overwrite=True)
left = Sequential()
left.add(Dense(nb_hidden, input_shape=(input_dim,)))
left.add(Activation('relu'))
right = Sequential()
right.add(Dense(nb_hidden, input_shape=(input_dim,)))
right.add(Activation('relu'))
add_shared_layer(Dense(nb_hidden), [left, right])
left.add(Dense(nb_hidden))
right.add(Dense(nb_hidden))
add_shared_layer(Dense(nb_hidden), [left, right])
model = Sequential()
model.add(Merge([left, right], mode='sum'))
model.add(Dense(nb_class))
model.add(Activation('softmax'))
model.load_weights(fname)
os.remove(fname)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
nloss = model.evaluate([X_test, X_test], y_test, verbose=0)
assert(loss == nloss)
def test_siamese_2():
(X_train, y_train), (X_test, y_test) = _get_test_data()
left = Sequential()
left.add(Dense(nb_hidden, input_shape=(input_dim,)))
left.add(Activation('relu'))
right = Sequential()
right.add(Dense(nb_hidden, input_shape=(input_dim,)))
right.add(Activation('relu'))
add_shared_layer(Dense(nb_hidden), [left, right])
left.add(Dense(nb_hidden))
right.add(Dense(nb_hidden))
add_shared_layer(Dense(nb_hidden), [left, right])
model = Sequential()
model.add(Merge([left, right], mode='sum'))
model.add(Dense(nb_class))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=0, validation_data=([X_test, X_test], y_test))
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=0, validation_data=([X_test, X_test], y_test))
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=0, validation_split=0.1)
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=0, validation_split=0.1)
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0)
model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0, shuffle=False)
loss = model.evaluate([X_test, X_test], y_test, verbose=0)
assert(loss < 0.8)
model.predict([X_test, X_test], verbose=0)
model.predict_classes([X_test, X_test], verbose=0)
model.predict_proba([X_test, X_test], verbose=0)
model.get_config(verbose=0)
# test weight saving
fname = 'test_siamese_2.h5'
model.save_weights(fname, overwrite=True)
left = Sequential()
left.add(Dense(nb_hidden, input_shape=(input_dim,)))
left.add(Activation('relu'))
right = Sequential()
right.add(Dense(nb_hidden, input_shape=(input_dim,)))
right.add(Activation('relu'))
add_shared_layer(Dense(nb_hidden), [left, right])
left.add(Dense(nb_hidden))
right.add(Dense(nb_hidden))
add_shared_layer(Dense(nb_hidden), [left, right])
model = Sequential()
model.add(Merge([left, right], mode='sum'))
model.add(Dense(nb_class))
model.add(Activation('softmax'))
model.load_weights(fname)
os.remove(fname)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
nloss = model.evaluate([X_test, X_test], y_test, verbose=0)
assert(loss == nloss)