def test_append_dense_layer():
model = CNN()
model.add_input_layer(shape=(256 * 256 * 3), name="input0")
model.append_dense_layer(num_nodes=100, activation='relu')
input = np.zeros((10, 256 * 256 * 3))
result = model.predict(input)
assert result.shape == (10, 100)
def test_append_conv2d_layer():
model = CNN()
model.add_input_layer(shape=(256, 256, 3), name="input0")
model.append_conv2d_layer(10, (3, 3), activation='relu')
input = np.zeros((20, 256, 256, 3))
out = model.predict(input)
assert (out.shape == (20, 256, 256, 10))
def test_add_flatten_layer():
model = CNN()
model.add_input_layer(shape=(256, 256, 3), name="input0")
model.append_flatten_layer(name='flatten')
input = np.zeros((10, 256, 256, 3))
out = model.predict(input)
assert out.shape == (10, 256 * 256 * 3)
def test_append_maxpooling2d_layer():
model = CNN()
model.add_input_layer(shape=(256, 256, 3), name="input0")
model.append_maxpooling2d_layer(pool_size=(2, 2),
padding="same",
strides=2,
name='pooling')
input = np.zeros((10, 256, 256, 3))
out = model.predict(input)
assert (out.shape == (10, 128, 128, 3))
def test_get_weights_without_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_weights_without_biases(layer_number=k + 1)
assert actual.shape == (previous_nodes, number_of_nodes)
previous_nodes = number_of_nodes
def test_get_weights_without_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_weights_without_biases(layer_number=k + 1)
assert actual.shape == (kernel_size, kernel_size, previous_depth,
number_of_filters)
previous_depth = number_of_filters
actual = my_cnn.get_weights_without_biases(layer_number=0)
assert actual is None
def test_get_weights_without_biases_3():
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_weights_without_biases(layer_number=k + 1)
assert actual.shape == (kernel_size, kernel_size, previous_depth,
number_of_filters)
previous_depth = number_of_filters
actual = my_cnn.get_weights_without_biases(layer_number=0)
assert actual is None
pool_size = np.random.randint(2, 5)
my_cnn.append_maxpooling2d_layer(pool_size=pool_size,
padding="same",
strides=2,
name="pool1")
actual = my_cnn.get_weights_without_biases(layer_name="pool1")
assert actual is None
my_cnn.append_flatten_layer(name="flat1")
actual = my_cnn.get_weights_without_biases(layer_name="flat1")
assert actual is None
my_cnn.append_dense_layer(num_nodes=10)
number_of_dense_layers = np.random.randint(2, 10)
previous_nodes = 10
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_weights_without_biases(layer_number=k +
number_of_conv_layers + 4)
# assert actual.shape == (previous_nodes, number_of_nodes)
previous_nodes = number_of_nodes
def test_train():
from tensorflow.keras.datasets import mnist
import tensorflow.keras as keras
batch_size = 128
num_classes = 10
epochs = 1
# input image dimensions
img_rows, img_cols = 28, 28
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 28, 28, 1)
x_test = x_test.reshape(10000, 28, 28, 1)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = CNN()
model.add_input_layer(shape=(28, 28, 1), name="Input")
model.append_conv2d_layer(32, kernel_size=(3, 3))
model.append_conv2d_layer(64, kernel_size=(3, 3))
model.append_maxpooling2d_layer(pool_size=(2, 2))
model.append_flatten_layer()
model.append_dense_layer(128, activation="relu")
model.append_dense_layer(num_classes, activation="softmax")
model.set_loss_function("categorical_crossentropy")
model.set_metric("accuracy")
model.set_optimizer("Adagrad")
model.train(x_train, y_train, batch_size=batch_size, num_epochs=epochs)
score = model.evaluate(x_test, y_test)
actual = np.array([0.06997422293154523, 0.9907])
# loss
np.testing.assert_almost_equal(actual[0], score[0], decimal=2)
# accuracy
np.testing.assert_almost_equal(actual[1], score[1], decimal=2)
def test_remove_last_layer():
from tensorflow.keras.datasets import cifar10
batch_size = 32
num_classes = 10
epochs = 100
data_augmentation = True
num_predictions = 20
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_cifar10_trained_model.h5'
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
number_of_train_samples_to_use = 100
X_train = X_train[0:number_of_train_samples_to_use, :]
y_train = y_train[0:number_of_train_samples_to_use]
my_cnn = CNN()
my_cnn.add_input_layer(shape=(32, 32, 3), name="input")
my_cnn.append_conv2d_layer(num_of_filters=16,
kernel_size=(3, 3),
padding="same",
activation='linear',
name="conv1")
my_cnn.append_maxpooling2d_layer(pool_size=2,
padding="same",
strides=2,
name="pool1")
my_cnn.append_conv2d_layer(num_of_filters=8,
kernel_size=3,
activation='relu',
name="conv2")
my_cnn.append_flatten_layer(name="flat1")
my_cnn.append_dense_layer(num_nodes=10, activation="relu", name="dense1")
my_cnn.append_dense_layer(num_nodes=2, activation="relu", name="dense2")
out = my_cnn.predict(X_train)
assert out.shape == (number_of_train_samples_to_use, 2)
my_cnn.remove_last_layer()
out = my_cnn.predict(X_train)
assert out.shape == (number_of_train_samples_to_use, 10)
def test_predict():
# some of these may be duplicated
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)])
# X = np.float32([[0.1, 0.2]])
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")
# my_cnn.append_dense_layer(num_nodes=5, activation='linear', name="layer12")
actual = my_cnn.predict(X)
assert np.array_equal(actual, np.array([[104., 102., 102., 102., 102.]]))
def test_load_and_save_model():
# Note: This test may take a long time to load the data
my_cnn = CNN()
my_cnn.load_a_model(model_name="VGG19")
# my_cnn.append_dense_layer(num_nodes=10)
w = my_cnn.get_weights_without_biases(layer_name="block5_conv4")
assert w.shape == (3, 3, 512, 512)
w = my_cnn.get_weights_without_biases(layer_number=-1)
assert w.shape == (4096, 1000)
my_cnn.append_dense_layer(num_nodes=10)
path = os.getcwd()
file_path = os.path.join(path, "my_model.h5")
my_cnn.save_model(model_file_name=file_path)
my_cnn.load_a_model(model_name="VGG16")
w = my_cnn.get_weights_without_biases(layer_name="block4_conv1")
assert w.shape == (3, 3, 256, 512)
my_cnn.load_a_model(model_file_name=file_path)
os.remove(file_path)
w = my_cnn.get_weights_without_biases(layer_number=-1)
assert w.shape == (1000, 10)
def test_set_weights_without_biases():
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')
w = my_cnn.get_weights_without_biases(layer_number=k + 1)
w_set = np.full_like(w, 0.2)
my_cnn.set_weights_without_biases(w_set, layer_number=k + 1)
w_get = my_cnn.get_weights_without_biases(layer_number=k + 1)
assert w_get.shape == w_set.shape
previous_depth = number_of_filters
pool_size = np.random.randint(2, 5)
my_cnn.append_maxpooling2d_layer(pool_size=pool_size,
padding="same",
strides=2,
name="pool1")
my_cnn.append_flatten_layer(name="flat1")
my_cnn.append_dense_layer(num_nodes=10)
number_of_dense_layers = np.random.randint(2, 10)
previous_nodes = 10
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)
w = my_cnn.get_weights_without_biases(layer_number=k +
number_of_conv_layers + 4)
w_set = np.full_like(w, 0.8)
my_cnn.set_weights_without_biases(w_set,
layer_number=k +
number_of_conv_layers + 4)
w_get = my_cnn.get_weights_without_biases(layer_number=k +
number_of_conv_layers + 4)
assert w_get.shape == w_set.shape
previous_nodes = number_of_nodes
def test_add_input_layer():
model = CNN()
out = model.add_input_layer(shape=(256, 256, 3), name="input0")
# no tests for this?
assert True