Example #1
0
 def test_tanh_1_forward(self):
     print('\n==================================')
     print('          Test tanh forward       ')
     print('==================================')
     x = np.linspace(-1.5, 1.5, num=30).reshape(6, 5)
     tanh = Tanh()
     out = tanh.forward(x)
     correct_out = [
         [-0.90514825, -0.88460402, -0.85993717, -0.83047189, -0.79548953],
         [-0.75425761, -0.70607365, -0.65032522, -0.58656543, -0.51459914],
         [-0.43457022, -0.34703513, -0.25300497, -0.15393885, -0.05167806],
         [0.05167806, 0.15393885, 0.25300497, 0.34703513, 0.43457022],
         [0.51459914, 0.58656543, 0.65032522, 0.70607365, 0.75425761],
         [0.79548953, 0.83047189, 0.85993717, 0.88460402, 0.90514825]
     ]
     e = rel_error(correct_out, out)
     print('Relative difference:', e)
     self.assertTrue(e <= 5e-7)
Example #2
0
def test_model(num_feat, num_classes):
    classifier = ClassifierModel()
    classifier.add_layer('FC-1', FCLayer(num_feat, 2))
    classifier.add_layer('Sigmoid', Sigmoid())
    classifier.add_layer('FC-2', FCLayer(2, 5))
    classifier.add_layer('ReLU', ReLU())
    classifier.add_layer('FC-3', FCLayer(5, 3))
    classifier.add_layer('tanh', Tanh())
    classifier.add_layer('FC-4', FCLayer(3, num_classes))
    classifier.add_layer('Softmax', SoftmaxLayer())
    return classifier
Example #3
0
    def test_tanh_2_backward(self):
        print('\n==================================')
        print('          Test tanh backward      ')
        print('==================================')

        np.random.seed(123)
        x = np.random.randn(5, 5)
        tanh = Tanh()
        out = tanh.forward(x)
        dx = tanh.backward(x, 0.0)

        correct_dx = [
            [-0.39900528, 0.42055529, 0.26147548, -0.26911137, -0.42115391],
            [0.22601191, -0.074565, -0.35876431, 0.34549378, -0.44238431],
            [-0.44192875, -0.0938645, 0.27373408, -0.43556065, -0.36680073],
            [-0.36170986, 0.10450339, 0.10753923, 0.41907822, 0.33386819],
            [0.44697633, 0.27393871, -0.43260586, 0.37333295, -0.349298]
        ]
        e = rel_error(correct_dx, dx)
        print('Relative difference:', e)
        self.assertTrue(e <= 5e-8)
Example #4
0
def test_model(num_feat, num_classes):
    classifier = CNN_Classifier()
    classifier.add_layer(
        'Conv-1', ConvolutionLayer(num_feat, 2, kernel_size=3, stride=1,
                                   pad=1))
    classifier.add_layer('ReLU', ReLU())
    classifier.add_layer(
        'Conv-2', ConvolutionLayer(2, 3, kernel_size=3, stride=1, pad=1))
    classifier.add_layer('tanh', Tanh())
    classifier.add_layer(
        'Conv-3', ConvolutionLayer(3, 3, kernel_size=3, stride=1, pad=0))
    classifier.add_layer('Sigmoid', Sigmoid())
    classifier.add_layer('Max-pool - 1',
                         MaxPoolingLayer(kernel_size=2, stride=1))
    classifier.add_layer('FC-4', FCLayer(12, num_classes))
    classifier.add_layer('Softmax', SoftmaxLayer())
    return classifier
Example #5
0
"""

# mnist / fashion_mnist
dataset = 'mnist'

# Hyper-parameters
num_epochs = 100
learning_rate = 0.001
reg_lambda = 1e-8
print_every = 10

batch_size = 128

# Add layers
model.add_layer('FC-1', FCLayer(784, 500))
model.add_layer('sigmoid-1', Tanh())
model.add_layer('FC-2', FCLayer(500, 500))
model.add_layer('sigmoid-2', Tanh())
model.add_layer('FC-3',FCLayer(500,10))
model.add_layer('Softmax Layer', SoftmaxLayer())

# =========================================================================
assert dataset in ['mnist', 'fashion_mnist']

# Dataset
if dataset == 'mnist':
    x_train, y_train, x_test, y_test = load_mnist('./data')
else:
    x_train, y_train, x_test, y_test = load_fashion_mnist('./data')

x_train, x_test = np.squeeze(x_train), np.squeeze(x_test)
Example #6
0
# Hyper-parameters
num_epochs = 80
learning_rate = 0.1
reg_lambda = 1e-3
print_every = 10

batch_size = 128

# Add layers - example 1
#model.add_layer('FC-1', FCLayer(784, 10))
#model.add_layer('Sigmoid', Sigmoid())
#model.add_layer('Softmax Layer', SoftmaxLayer())

# Add layers - example 2
model.add_layer('FC-1', FCLayer(784, 200))
model.add_layer('Tanh1', Tanh())
model.add_layer('FC-2', FCLayer(200, 200))
model.add_layer('Tanh2', Tanh())
model.add_layer('FC-3', FCLayer(200, 10))
model.add_layer('Softmax Layer', SoftmaxLayer())

# =========================================================================
assert dataset in ['mnist', 'fashion_mnist']

# Dataset
if dataset == 'mnist':
    x_train, y_train, x_test, y_test = load_mnist('./data')
else:
    x_train, y_train, x_test, y_test = load_fashion_mnist('./data')

x_train, x_test = np.squeeze(x_train), np.squeeze(x_test)