def test_train_transpose(self):
"""Train using Conv2DTranspose"""
x = np.random.randn(64 * 64).reshape((64, 64))
y = np.random.randn(64 * 64).reshape((64, 64))
X = np.stack((x, y), -1)
X = np.expand_dims(X, 0)
Y = X
inputs = Input(shape=(64, 64, 2))
conv1 = conn.ComplexConv2D(
filters=2, # = 4 Keras filters
kernel_size=3,
strides=2,
padding="same",
transposed=False)(inputs)
outputs = conn.ComplexConv2D(
filters=1, # = 2 Keras filters => 1 complex layer
kernel_size=3,
strides=2,
padding="same",
transposed=True)(conv1)
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam',
loss='mean_squared_error',
metrics=['accuracy'])
model.fit(X, Y, batch_size=1, epochs=10)
def test_outputs_transpose(self):
"""Test computed shape of transposed convolution output"""
layer = conn.ComplexConv2D(filters=2,
kernel_size=3,
strides=2,
padding="same",
transposed=True)
input_shape = (None, 64, 64, 4)
true = (None, 128, 128, 4)
calc = layer.compute_output_shape(input_shape)
assert true == calc
def test_conv2Dforward(self):
"""Test shape of model output, forward"""
inputs = Input(shape=(128, 128, 2))
outputs = conn.ComplexConv2D(filters=4,
kernel_size=3,
strides=2,
padding="same",
transposed=False)(inputs)
model = Model(inputs=inputs, outputs=outputs)
true = (None, 64, 64, 8)
calc = model.output_shape
assert true == calc
def test_outputs_forward(self):
"""Test computed shape of forward convolution output"""
layer = conn.ComplexConv2D(
filters=4,
kernel_size=3,
strides=2,
padding="same",
transposed=False)
input_shape = (None, 128, 128, 2)
true = (None, 64, 64, 8)
calc = layer.compute_output_shape(input_shape)
self.assertEqual(true, calc)
def test_conv2Dtranspose(self):
"""Test shape of model output, transposed"""
inputs = Input(shape=(64, 64, 20)) # = 10 CDN filters
outputs = conn.ComplexConv2D(
filters=2, # = 4 Keras filters
kernel_size=3,
strides=2,
padding="same",
transposed=True)(inputs)
model = Model(inputs=inputs, outputs=outputs)
true = (None, 128, 128, 4)
calc = model.output_shape
assert true == calc