expected_output_shape = layer.get_output_shape_for(input_shape)
actual_output = model.predict(input_data)
actual_output_shape = actual_output.shape
assert expected_output_shape == actual_output_shape
if expected_output is not None:
assert_allclose(actual_output, expected_output, rtol=1e-3)
// test serialization
After Change
if e is None:
input_data_shape[i] = np.random.randint(1, 4)
input_data = (10 * np.random.random(input_data_shape))
input_data = input_data.astype(input_dtype)
elif input_shape is None:
input_shape = input_data.shape
if expected_output_dtype is None:
expected_output_dtype = input_dtype
// instantiation
layer = layer_cls(**kwargs)
// test get_weights , set_weights
weights = layer.get_weights()
layer.set_weights(weights)
// test and instantiation from weights
if "weights" in inspect.getargspec(layer_cls.__init__):
kwargs["weights"] = weights
layer = layer_cls(**kwargs)
// test in functional API
if fixed_batch_size:
x = Input(batch_shape=input_shape, dtype=input_dtype)
else:
x = Input(shape=input_shape[1:], dtype=input_dtype)
y = layer(x)
assert K.dtype(y) == expected_output_dtype
model = Model(input=x, output=y)
model.compile("rmsprop", "mse")
expected_output_shape = layer.get_output_shape_for(input_shape)
actual_output = model.predict(input_data)
actual_output_shape = actual_output.shape
for expected_dim, actual_dim in zip(expected_output_shape,
actual_output_shape):
if expected_dim is not None:
assert expected_dim == actual_dim
if expected_output is not None:
assert_allclose(actual_output, expected_output, rtol=1e-3)
// test serialization
