def compare_layer(self,
klayer,
zlayer,
input_data,
weight_converter=None,
is_training=False,
rtol=1e-6,
atol=1e-6):
"""
Compare forward results for Keras layer against Zoo Keras API layer.
"""
from keras.models import Sequential as KSequential
from zoo.pipeline.api.keras.models import Sequential as ZSequential
zmodel = ZSequential()
zmodel.add(zlayer)
kmodel = KSequential()
kmodel.add(klayer)
koutput = kmodel.predict(input_data)
from zoo.pipeline.api.keras.layers import BatchNormalization
if isinstance(zlayer, BatchNormalization):
k_running_mean = K.eval(klayer.running_mean)
k_running_std = K.eval(klayer.running_std)
zlayer.set_running_mean(k_running_mean)
zlayer.set_running_std(k_running_std)
if kmodel.get_weights():
zmodel.set_weights(weight_converter(klayer, kmodel.get_weights()))
zmodel.training(is_training)
zoutput = zmodel.forward(input_data)
self.assert_allclose(zoutput, koutput, rtol=rtol, atol=atol)
def test_regularizer(self):
model = ZSequential()
model.add(
ZLayer.Dense(16,
W_regularizer=regularizers.l2(0.001),
activation='relu',
input_shape=(10000, )))
model.summary()
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['acc'])
def test_save_load_Sequential(self):
zmodel = ZSequential()
dense = ZLayer.Dense(10, input_dim=5)
zmodel.add(dense)
tmp_path = create_tmp_path()
zmodel.saveModel(tmp_path, None, True)
model_reloaded = Net.load(tmp_path)
input_data = np.random.random([10, 5])
y = np.random.random([10, 10])
model_reloaded.compile(optimizer="adam", loss="mse")
model_reloaded.fit(x=input_data, y=y, batch_size=8, nb_epoch=1)
def test_merge_method_seq_concat(self):
zx1 = ZLayer.Input(shape=(10, ))
zx2 = ZLayer.Input(shape=(10, ))
zy1 = ZLayer.Dense(12, activation="sigmoid")(zx1)
zbranch1_node = ZModel(zx1, zy1)(zx1)
zbranch2 = ZSequential()
zbranch2.add(ZLayer.Dense(12, input_dim=10))
zbranch2_node = zbranch2(zx2)
zz = ZLayer.merge([zbranch1_node, zbranch2_node], mode="concat")
zmodel = ZModel([zx1, zx2], zz)
kx1 = KLayer.Input(shape=(10, ))
kx2 = KLayer.Input(shape=(10, ))
ky1 = KLayer.Dense(12, activation="sigmoid")(kx1)
kbranch1_node = KModel(kx1, ky1)(kx1)
kbranch2 = KSequential()
kbranch2.add(KLayer.Dense(12, input_dim=10))
kbranch2_node = kbranch2(kx2)
kz = KLayer.merge([kbranch1_node, kbranch2_node], mode="concat")
kmodel = KModel([kx1, kx2], kz)
input_data = [np.random.random([2, 10]), np.random.random([2, 10])]
self.compare_layer(kmodel, zmodel, input_data, self.convert_two_dense)