def test_printer(self, b, w, h, c):
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.summary()
def test_forward(self, b, w, h, c):
input = np.random.uniform(low=-10, high=10.,
size=(b, w, h, c)).astype(float)
# 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.summary()
assert net._fitted == False
net._fitted = True # False control
# FORWARDS
fwd_out_numpynet = net.predict(X=input)
fwd_out_keras = model.predict(x=input, batch_size=b)
np.testing.assert_allclose(fwd_out_keras,
fwd_out_numpynet,
rtol=1e-5,
atol=1e-8)
model.add(BatchNorm_layer())
model.add(Connected_layer(outputs=num_classes, activation='Linear'))
model.add(Softmax_layer(spatial=True, groups=1, temperature=1.))
# model.add(Cost_layer(cost_type=cost_type.mse))
# model.compile(optimizer=SGD(lr=0.01, decay=0., lr_min=0., lr_max=np.inf))
model.compile(optimizer=Adam(), metrics=[accuracy])
print('*************************************')
print('\n Total input dimension: {}'.format(X_train.shape), '\n')
print('**************MODEL SUMMARY***********')
model.summary()
print('\n***********START TRAINING***********\n')
# Fit the model on the training set
model.fit(X=X_train, y=y_train, max_iter=10, verbose=True)
print('\n***********START TESTING**************\n')
# Test the prediction with timing
loss, out = model.evaluate(X=X_test, truth=y_test, verbose=True)
truth = from_categorical(y_test)
predicted = from_categorical(out)
accuracy = mean_accuracy_score(truth, predicted)