def test_option_type(self):
input1 = Input(n_channels=1, width=28, height=28)
conv1 = Conv2d(2)(input1)
conv2 = Conv2d(2)(input1)
concat1 = Concat()([conv1, conv2])
output1 = OutputLayer(n=2)(concat1)
model1 = Model(conn=self.s, inputs=[input1], outputs=output1)
model1.compile()
model1.print_summary()
self.assertTrue(model1.count_params() == 6314)
model2 = Model(conn=self.s, inputs=input1, outputs=output1)
model2.compile()
model2.print_summary()
model2.plot_network()
self.assertTrue(model2.count_params() == 6314)
def test_submodel_as_inputs_network(self):
inputs1 = Input(1, 53, 53, scale=1.0 / 255, name='InputLayer_1')
# inputs2 = InputLayer(1, 28, 28, scale = 1.0 / 255, name = 'InputLayer_2')
dense1 = Dense(10, name='d1')(inputs1)
dense2 = Dense(12, name='d2')(dense1)
dense3 = Dense(n=128, name='d3')(dense2)
model_dense = Model(self.s, inputs=inputs1, outputs=dense3)
inputs2 = Input(1, 53, 53, scale=1.0 / 255, name='InputLayer_2')
# inputs2 = InputLayer(1, 28, 28, scale = 1.0 / 255, name = 'InputLayer_2')
dense4 = Dense(10, name='d4')(inputs2)
dense5 = Dense(12, name='d5')(dense4)
dense6 = Dense(n=128, name='d6')(dense5)
model_dense2 = Model(self.s, inputs=inputs2, outputs=dense6)
inputs3 = Input(1, 53, 53, scale=1.0 / 255, name='InputLayer_3')
inputs4 = Input(1, 53, 53, scale=1.0 / 255, name='InputLayer_4')
out3 = model_dense(inputs3)
out4 = model_dense2(inputs4)
out5 = [out4[0], out3[0]]
concat1 = Dense(20, name='d7')(out5)
output1 = OutputLayer(n=10, name='OutputLayer_1')(concat1)
model = Model(self.s, inputs=[inputs3, inputs4], outputs=output1)
model.compile()
model.print_summary()
self.assertTrue(model.count_params() == 62262)
def test_submodel_multiple_inputs_network(self):
inputs1 = Input(1, 53, 53, scale=1.0 / 255, name='InputLayer_1')
# inputs2 = InputLayer(1, 28, 28, scale = 1.0 / 255, name = 'InputLayer_2')
dense1 = Dense(10, name='d1')(inputs1)
dense2 = Dense(12, name='d2')(dense1)
dense3 = Dense(n=128, name='d3')(dense2)
# model_dense = Model(self.s, inputs=inputs1, outputs= dense3)
inputs2 = Input(1, 53, 53, scale=1.0 / 255, name='InputLayer_2')
# inputs2 = InputLayer(1, 28, 28, scale = 1.0 / 255, name = 'InputLayer_2')
dense4 = Dense(10, name='d4')(inputs2)
dense5 = Dense(12, name='d5')(dense4)
dense6 = Dense(n=128, name='d6')(dense5)
dense7 = Dense(n=190, name='cat1')([dense3, dense6])
model_dense = Model(self.s,
inputs=[inputs1, inputs2],
outputs=[dense7])
input3 = Input(1, 53, 53, scale=1.0 / 255, name='InputLayer_3')
input4 = Input(1, 53, 53, scale=1.0 / 255, name='InputLayer_4')
out = model_dense([input3, input4])
dense7 = Dense(20, name='d7')(out)
output1 = OutputLayer(n=10, name='OutputLayer_1')(dense7)
model = Model(self.s, inputs=[input3, input4], outputs=output1)
model.compile()
model.print_summary()
self.assertTrue(model.count_params() == 87822)
def test_single_src_layer_list_fc(self):
inputs = Input(1, 28, 28, scale=1.0 / 255, name='InputLayer_1')
fc1 = Dense(n=128)(inputs)
fc3 = Dense(n=64)([fc1])
output1 = OutputLayer(n=10, name='OutputLayer_1')(fc3)
model = Model(self.s, inputs=inputs, outputs=output1)
model.compile()
self.assertTrue(model.count_params() == 109194)
def test_without_variable(self):
input1 = Input(n_channels=1, width=28, height=28)
conv1 = Conv2d(2)(Conv2d(2)(input1))
output1 = OutputLayer(n=2)(conv1)
model1 = Model(conn=self.s, inputs=[input1], outputs=[output1])
model1.compile()
model1.print_summary()
self.assertTrue(model1.count_params() == 3196)
def test_non_list_src_layers(self):
inputs = Input(1, 28, 28, scale=1.0 / 255, name='InputLayer_1')
fc1 = Dense(n=128)(inputs)
fc2 = Dense(n=64)(fc1)
output1 = OutputLayer(n=10, name='OutputLayer_1')([fc1, fc2])
model = Model(self.s, inputs=inputs, outputs=output1)
model.compile()
model.print_summary()
self.assertTrue(model.count_params() == 109834)
def test_multi_src_layer_fc(self):
inputs = Input(1, 28, 28, scale=1.0 / 255, name='InputLayer_1')
fc1 = Dense(n=128, name='dense1')(inputs)
fc2 = Dense(n=64, name='dense2')(fc1)
fc3 = Dense(n=64, name='dense3')([fc1, fc2])
output1 = OutputLayer(n=10, name='OutputLayer_1')(fc3)
model = Model(self.s, inputs=inputs, outputs=output1)
model.compile()
self.assertTrue(model.count_params() == 117386)
def test_concat(self):
input1 = Input(n_channels=1, width=28, height=28)
input2 = Input(n_channels=3, width=28, height=28)
conv1 = Conv2d(2)(input1)
conv2 = Conv2d(2)(input1)
conv3 = Conv2d(2)(input2)
output2 = OutputLayer(n=2)(conv3)
concat1 = Concat()([conv1, conv2, conv3])
output1 = OutputLayer(n=2)(concat1)
model1 = Model(conn=self.s, inputs=[input1, input2], outputs=[output1, output2])
model1.compile()
model1.print_summary()
self.assertTrue(model1.count_params() == 12644)
def test_submodel_in_middle_network(self):
inputs1 = Input(1, 53, 53, scale=1.0 / 255, name='InputLayer_1')
inputs2 = Input(1, 28, 28, scale=1.0 / 255, name='InputLayer_2')
dense1 = Dense(10, name='d1')(inputs2)
dense2 = Dense(12, name='d2')(dense1)
dense3 = Dense(n=128, name='d3')(dense2)
model_dense = Model(self.s, inputs=inputs2, outputs=dense3)
fore = model_dense(inputs1)
concat1 = Dense(20, name='d7')(fore)
output1 = OutputLayer(n=10, name='OutputLayer_1')(concat1)
model = Model(self.s, inputs=[inputs1], outputs=output1)
model.compile()
model.print_summary()
self.assertTrue(model.count_params() == 32516)
def test_submodel_as_input_network(self):
inputs1 = Input(1, 53, 53, scale=1.0 / 255, name='InputLayer_1')
# inputs2 = InputLayer(1, 28, 28, scale = 1.0 / 255, name = 'InputLayer_2')
dense1 = Dense(10, name='dense1')(inputs1)
dense2 = Dense(12, name='dense2')(dense1)
dense3 = Dense(n=128, name='dense3')(dense2)
model_dense = Model(self.s, inputs=inputs1, outputs=dense3)
inputs2 = Input(1, 53, 53, scale=1.0 / 255, name='InputLayer_2')
out_submodel = model_dense(inputs2)
concat1 = Dense(20, 'concat1')(out_submodel)
output1 = OutputLayer(n=10, name='OutputLayer_1')(concat1)
model = Model(self.s, inputs=[inputs2], outputs=output1)
model.compile()
model.print_summary()
self.assertTrue(model.count_params() == 32516)
