def test_backward(self, b, w, h, c):
# TODO: test backward correctly
input = np.random.uniform(low=-10, high=10., size=(b, w, h, c))
tf_input = tf.Variable(input)
# init keras model
inp = Input(batch_shape=(b, w, h, c))
x = Activation(activation='relu')(inp)
y = Activation(activation='tanh')(x)
Concat = Concatenate(axis=-1)([x, y]) # concatenate of x and y
model = Model(inputs=[inp], outputs=Concat)
model.compile(optimizer='sgd', loss='mse')
# init NumPyNet model
net = Network(batch=b, input_shape=(w, h, c))
net.add(Activation_layer(activation='relu')) # layer 1
net.add(Activation_layer(activation='tanh')) # layer 2
net.add(Route_layer(input_layers=(1, 2), by_channels=True))
net.add(
Cost_layer(cost_type='mse',
scale=1.,
ratio=0.,
noobject_scale=1.,
threshold=0.,
smoothing=0.))
net.compile(optimizer=SGD())
net._fitted = True
# FORWARDS
fwd_out_numpynet = net.predict(X=input)
with tf.GradientTape() as tape:
preds = model(tf_input)
grads = tape.gradient(preds, tf_input)
fwd_out_keras = preds.numpy()
delta_keras = grads.numpy()
np.testing.assert_allclose(fwd_out_keras,
fwd_out_numpynet,
rtol=1e-5,
atol=1e-8)
net._fitted = False
# BACKWARD
net._net[3].delta = np.ones(shape=fwd_out_numpynet.shape, dtype=float)
net._backward(X=input)
delta_numpynet = net._net[0].delta
assert delta_numpynet.shape == delta_keras.shape
def test_route_layer(): np.random.seed(123) batch, w, h, c = (1, 5, 5, 3) input = np.random.uniform(low=-10, high=10. ,size=(batch, w, h, c)) # from -10 to 10 to see both the effect of Relu and TanH activation # init keras model inp = Input(shape=(w, h, c), batch_shape=(batch, w, h, c)) x = Activation(activation='relu')(inp) y = Activation(activation='tanh')(x) Concat = Concatenate( axis=-1)([x, y]) # concatenate of x and y model = Model(inputs=[inp], outputs=Concat) # init NumPyNet model net = Network(batch=batch, input_shape=(w, h, c)) net.add(Activation_layer(activation='relu')) # layer 1 net.add(Activation_layer(activation='tanh')) # layer 2 net.add(Route_layer(input_layers=(1,2), by_channels=True)) net._fitted = True # False control # FORWARDS fwd_out_numpynet = net.predict(X=input) fwd_out_keras = model.predict(x=input, batch_size=batch) assert np.allclose(fwd_out_keras, fwd_out_numpynet) # ok net.fitted = False # the correct state of the network # BACKWARD # try some derivatives gradient = K.gradients(model.output, model.inputs) func = K.function(model.inputs + model.outputs ,gradient) delta_keras = func([input])[0] net._net[3].delta = np.ones(shape=fwd_out_numpynet.shape) net._backward(X=input) delta_numpynet = net._net[0].delta