"""Test pygad.cnn layers and Model class."""

# Dummy data

data_inputs = numpy.random.uniform(0, 1, (4, 10, 10, 3))

# The test do not care about the outputs predicted by the network.

# data_outputs = numpy.array([0, 1, 1, 0])

input_layer = pygad.cnn.Input2D(input_shape=(10, 10, 3))

conv_layer = pygad.cnn.Conv2D(num_filters=2,

kernel_size=3,

previous_layer=input_layer,

activation_function="relu")

max_pooling_layer = pygad.cnn.MaxPooling2D(pool_size=2,

previous_layer=conv_layer,

stride=2)

flatten_layer = pygad.cnn.Flatten(previous_layer=max_pooling_layer)

dense_layer = pygad.cnn.Dense(num_neurons=2,

previous_layer=flatten_layer,

activation_function="softmax")

model = pygad.cnn.Model(last_layer=dense_layer,

epochs=1,

learning_rate=0.01)

# Test predict

predictions = model.predict(data_inputs=data_inputs)

assert len(predictions) == 4

# Test summary (just to ensure it doesn't crash)

model.summary()

# Test layers_weights

weights = pygad.cnn.layers_weights(model)

assert isinstance(weights, list)

assert len(weights) > 0

# Test layers_weights_as_vector

weights_vector = pygad.cnn.layers_weights_as_vector(model)

assert isinstance(weights_vector, numpy.ndarray)

assert weights_vector.ndim == 1