def test_get_biases_2():
my_cnn = CNN()
image_size=(np.random.randint(32,100),np.random.randint(20,100),np.random.randint(3,10))
number_of_conv_layers=np.random.randint(2,10)
my_cnn.add_input_layer(shape=image_size,name="input")
previous_depth=image_size[2]
for k in range(number_of_conv_layers):
number_of_filters = np.random.randint(3, 100)
kernel_size= np.random.randint(3,9)
my_cnn.append_conv2d_layer(num_of_filters=number_of_filters,
kernel_size=(kernel_size,kernel_size),
padding="same", activation='linear')
actual = my_cnn.get_biases(layer_number=k+1)
assert (actual.shape == (number_of_filters,)) or (actual.shape == (number_of_filters,1))
previous_depth=number_of_filters
actual = my_cnn.get_biases(layer_number=0)
assert actual is None
def test_get_biases_1():
my_cnn = CNN()
input_size=np.random.randint(32,100)
number_of_dense_layers=np.random.randint(2,10)
my_cnn.add_input_layer(shape=input_size,name="input")
previous_nodes=input_size
for k in range(number_of_dense_layers):
number_of_nodes = np.random.randint(3, 100)
kernel_size= np.random.randint(3,9)
my_cnn.append_dense_layer(num_nodes=number_of_nodes)
actual = my_cnn.get_biases(layer_number=k+1)
assert (actual.shape == (number_of_nodes,)) or (actual.shape == (number_of_nodes,1))
previous_nodes=number_of_nodes
def test_predict():
X = np.float32([[0.1, 0.2, 0.3, 0.4, 0.5, -0.1, -0.2, -0.3, -0.4, -0.5]])
X = np.float32([[0.1, 0.2, 0.3, 0.4, 0.5, 0, 0, 0, 0, 0]])
X = np.float32([np.linspace(0, 10, num=10)])
my_cnn = CNN()
my_cnn.add_input_layer(shape=(10, ), name="input0")
my_cnn.append_dense_layer(num_nodes=5, activation='linear', name="layer1")
w = my_cnn.get_weights_without_biases(layer_name="layer1")
w_set = np.full_like(w, 2)
my_cnn.set_weights_without_biases(w_set, layer_name="layer1")
b = my_cnn.get_biases(layer_name="layer1")
b_set = np.full_like(b, 2)
b_set[0] = b_set[0] * 2
my_cnn.set_biases(b_set, layer_name="layer1")
actual = my_cnn.predict(X)
assert np.array_equal(actual, np.array([[104., 102., 102., 102., 102.]]))
