def test_fc_layer_1_forward(self):
print('\n==================================')
print(' Test FC layer forward ')
print('==================================')
np.random.seed(123)
in_dim, out_dim = 5, 7
x = np.random.randn(5, in_dim)
fc_layer = FCLayer(in_dim, out_dim)
fc_out = fc_layer.forward(x)
correct_out = [[
-2.61587108, -1.61266299, -0.0669421, -2.54145585, 1.28682999,
1.1125263, -0.68786543
],
[
4.32876618, 2.55437969, -2.24952545, 1.52993583,
-3.05818339, -3.06667247, -0.76003621
],
[
-0.09513516, -1.28239277, -1.76747871, -1.4526619,
0.94121269, 0.3876754, 0.23826321
],
[
-0.57981942, -2.07002539, 0.17966919, 0.89441346,
0.64115475, 1.20433427, 0.8938306
],
[
0.27645953, 0.52337315, 1.11057732, 0.96039962,
-3.09284442, -1.62932547, -1.67869927
]]
e = rel_error(correct_out, fc_out)
print('Relative difference:', e)
self.assertTrue(e <= 5e-8)
def test_fc_layer_2_backward(self):
print('\n==================================')
print(' Test FC layer backward ')
print('==================================')
np.random.seed(123)
in_dim, out_dim = 5, 7
x = np.random.randn(5, in_dim)
fc_layer = FCLayer(in_dim, out_dim)
fc_out = fc_layer.forward(x)
d_prev = np.random.randn(*fc_out.shape)
dx = fc_layer.backward(d_prev, 0.01)
dw = fc_layer.dw
db = fc_layer.db
correct_dx = [
[-0.38227102, 1.17252013, 0.12788217, -4.28457506, 2.23774842],
[1.39190197, 1.971887, 0.19018213, -1.17233471, -0.07123409],
[0.3228319, 0.29698036, -1.09577096, -0.24408399, -0.85236351],
[3.38556938, 0.8984432, -2.31575898, -0.52158556, -1.97062228],
[-1.37065069, -0.89799099, -0.33241037, 0.8274308, 0.10209643]
]
correct_dw = [[
3.72943234, -4.14214785, 0.75059198, 1.50031574, -1.74298193,
3.31825892, -2.45157517
],
[
1.57658455, 7.50683015, 3.36883864, -5.0322751,
1.38427174, -1.71863439, -0.59066825
],
[
-6.74286032, 5.76269827, 0.07986016, -0.26893331,
-3.03193173, -5.36704897, 0.01743584
],
[
4.00285798, -1.51490194, 2.20612597, 0.87921304,
-3.53759088, 1.92435603, -3.85981001
],
[
-1.2963323, 0.55252352, -1.29937229, 1.89050352,
-0.36306998, -2.51168386, 0.3259339
]]
correct_db = [
-1.73065675, 2.46168233, 2.06794376, -2.56596612, -1.15623993,
0.80887778, -1.34400948
]
dx_e = rel_error(correct_dx, dx)
dw_e = rel_error(correct_dw, dw)
db_e = rel_error(correct_db, db)
print('Relative difference dx:', dx_e)
print('Relative difference dw:', dw_e)
print('Relative difference db:', db_e)
self.assertTrue(dx_e <= 5e-8)
self.assertTrue(dw_e <= 5e-6)
self.assertTrue(db_e <= 5e-8)
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
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
CNN.add_layer('Conv Layer - 1', ConvolutionLayer(in_channels=in_channnel, out_channels=8, kernel_size=3, pad=1))
# ReLU Layer
CNN.add_layer('ReLU Layer - 1', ReLU())
# Convolution Layer
CNN.add_layer('Conv Layer - 2', ConvolutionLayer(in_channels=8, out_channels=8, kernel_size=3, pad=1))
# ReLU Layer
CNN.add_layer('ReLU Layer - 2', ReLU())
# Max-pool Layer
CNN.add_layer('Max-Pool Layer', MaxPoolingLayer(kernel_size=2, stride=2))
# FC Layer
CNN.add_layer('FC Example Layer - 1', FCLayer(input_dim=1568, output_dim=500))
# FC Layer
CNN.add_layer('FC Example Layer - 2', FCLayer(input_dim=500, output_dim=5))
# Softmax Layer
# 이 layer는 항상 마지막에 추가
CNN.add_layer('Softmax Layer', SoftmaxLayer())
# Model Architecture 출력
CNN.summary()
# Hyper-parameters
num_epochs = 10
learning_rate = 0.01
print_every = 1
def test_fc_layer_3_update(self):
print('\n==================================')
print(' Test fc layer update ')
print('==================================')
np.random.seed(123)
in_dim, out_dim = 5, 7
x = np.random.randn(5, in_dim)
fc_layer = FCLayer(in_dim, out_dim)
before_w = np.array(fc_layer.w, copy=True)
before_b = np.array(fc_layer.b, copy=True)
fc_out = fc_layer.forward(x)
d_prev = np.random.randn(*fc_out.shape)
dx = fc_layer.backward(d_prev, 0.01)
fc_layer.update(learning_rate=0.05)
after_w = fc_layer.w
after_b = fc_layer.b
correct_before_w = [[
-0.40334947, 0.5737037, -0.90357701, -0.08858724, -0.54502165,
-0.16166788, -1.76998316
],
[
-1.12041591, -0.44264123, 0.58657875,
-0.10981685, 0.00179992, 0.43527026,
-0.55626763
],
[
0.17938167, -0.50935851, -1.09267413,
-0.24722674, 0.36290669, 0.21414252,
-0.00748226
],
[
1.51306465, 0.26114858, 0.619007, 1.41552614,
-0.81845142, -0.65698735, 1.10282044
],
[
-0.50473919, 0.01877332, 0.6762948, 0.56333218,
1.10988747, 0.94592841, 0.67634331
]]
correct_before_b = [0., 0., 0., 0., 0., 0., 0.]
correct_after_w = [[
-0.58982108, 0.78081109, -0.94110661, -0.16360302, -0.45787255,
-0.32758083, -1.6474044
],
[
-1.19924514, -0.81798274, 0.41813681,
0.14179691, -0.06741367, 0.52120198, -0.52673421
],
[
0.51652469, -0.79749342, -1.09666714,
-0.23378007, 0.51450328, 0.48249497, -0.00835405
],
[
1.31292175, 0.33689368, 0.5087007, 1.37156548,
-0.64157188, -0.75320515, 1.29581094
],
[
-0.43992258, -0.00885285, 0.74126341,
0.46880701, 1.12804097, 1.0715126, 0.66004662
]]
correct_after_b = [
0.08653284, -0.12308412, -0.10339719, 0.12829831, 0.057812,
-0.04044389, 0.06720047
]
before_w_e = rel_error(correct_before_w, before_w)
before_b_e = rel_error(correct_before_b, before_b)
after_w_e = rel_error(correct_after_w, after_w)
after_b_e = rel_error(correct_after_b, after_b)
print('Relative difference before_w:', before_w_e)
print('Relative difference before_b:', before_b_e)
print('Relative difference after_w :', after_w_e)
print('Relative difference after_b :', after_b_e)
self.assertTrue(before_w_e <= 5e-6)
self.assertTrue(before_b_e <= 1e-11)
self.assertTrue(after_w_e <= 5e-7)
self.assertTrue(after_b_e <= 5e-8)
max_pool = MaxPoolingLayer(kernel_size, stride=kernel_size)
x = np.array([[5, 1, 3, 10], [-1, 0, 4, -1], [-10, -1, 11, 1], [0, 1, -5, 3]],
dtype=np.float32).reshape(1, 1, 4, 4)
pooled = max_pool.forward(x)
d_pool = max_pool.backward(np.ones_like(pooled))
print('Difference pool out: ', (correct_pooled - pooled).sum())
print('Difference d_pool: ', (correct_d_pool - d_pool).sum())
print()
# ===========================================================================
print(
'=============================== 5. FC Layer ==============================='
)
fc_layer = FCLayer(2, 2)
fc_layer.W = np.array([[-2.0, 1.25], [0.01, -1.0]])
fc_layer.b = np.array([-1.0, 1.0])
temp6 = np.array([[-1, 3], [0.0, 1.0]])
temp7 = np.array([[-1, 3], [0.0, 1.0]])
fc_forward = fc_layer.forward(temp6)
fc_backward = fc_layer.backward(temp7)
fc_dW = fc_layer.dW
fc_db = fc_layer.db
# Difference should be
# => in the order of 1e-15 for forward
# => 0.0 for Backward, dW, db
print('Forward difference: ', (correct_fc_forward - fc_forward).sum())
print('Backward difference: ', (correct_fc_backward - fc_backward).sum())
model.add_layer('Softmax Layer', SoftmaxLayer())
"""
# 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')
# 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')
###########################################################################
# Add Layers, Layer 추가 예제
# 아래 예시를 참고하여 과제에 주어진 대로 (혹은 과제와 별개로 원하는 대로) Layer를 추가하세요.
# Convolution Layer
CNN.add_layer('Conv Layer 1', ConvolutionLayer(in_channels=in_channnel, out_channels=8, kernel_size=3, pad=1))
CNN.add_layer('ReLU 1', ReLU())
CNN.add_layer('Conv Layer 2', ConvolutionLayer(in_channels=8, out_channels=8, kernel_size=3, pad=1))
CNN.add_layer('ReLU 2', ReLU())
# Max-pool Layer
CNN.add_layer('Max-Pool Layer', MaxPoolingLayer(kernel_size=2, stride=2))
# FC Layer
CNN.add_layer('FC Layer 1', FCLayer(input_dim=1568, output_dim=500))
CNN.add_layer('FC Layer 2', FCLayer(input_dim=500, output_dim=5))
# Softmax Layer
# 이 layer는 항상 마지막에 추가
CNN.add_layer('Softmax Layer', SoftmaxLayer())
# Model Architecture 출력
CNN.summary()
# Hyper-parameters
num_epochs = 1
learning_rate = 0.005
print_every = 1
# =========================================================================
model.add_layer('Softmax Layer', SoftmaxLayer())
"""
# mnist / fashion_mnist
dataset = 'fashion_mnist'
# Hyper-parameters
num_epochs = 100
learning_rate = 0.02
reg_lambda = 1e-4
print_every = 10
batch_size = 128
# Add layers
model.add_layer('FC-1', FCLayer(784, 128))
model.add_layer('Tanh1', Tanh())
model.add_layer('FC-2', FCLayer(128, 32))
model.add_layer('Tanh2', Tanh())
model.add_layer('FC-3', FCLayer(32, 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')
CNN.add_layer(
'Conv Example Layer',
ConvolutionLayer(in_channels=in_channnel,
out_channels=16,
kernel_size=5,
pad=1))
# ReLU Layer
CNN.add_layer('ReLU Example Layer', ReLU())
# Max-pool Layer
CNN.add_layer('Max-Pool Example Layer', MaxPoolingLayer(kernel_size=3,
stride=3))
# FC Layer
CNN.add_layer('FC Example Layer', FCLayer(input_dim=1234, output_dim=123))
# Softmax Layer
# 이 layer는 항상 마지막에 추가
CNN.add_layer('Softmax Layer', SoftmaxLayer())
# Model Architecture 출력
CNN.summary()
# Hyper-parameters
num_epochs = None
learning_rate = None
print_every = None
# =========================================================================
train_accuracy = []
valid_accuracy = []
best_epoch = -1
best_acc = -1
best_model = None
# =============================== EDIT HERE ===============================
# Add layers
CNN.add_layer('Conv-1', ConvolutionLayer(in_channels=in_channels, out_channels=4, kernel_size=3, pad=1))
CNN.add_layer('ReLU-1',ReLU())
CNN.add_layer('Conv-2', ConvolutionLayer(in_channels=4, out_channels=4, kernel_size=3, pad=1))
CNN.add_layer('ReLU-2',ReLU())
CNN.add_layer('Max-pool-1',MaxPoolingLayer(2,2))
CNN.add_layer('FC-1',FCLayer(784,500))
CNN.add_layer('ReLU-3',ReLU())
CNN.add_layer('FC-2',FCLayer(500,10))
CNN.add_layer('Softmax Layer',SoftmaxLayer())
# =========================================================================
CNN.summary()
print('Training Starts...')
num_batch = int(np.ceil(num_train / batch_size))
for epoch in range(1, num_epochs + 1):
start = time.time()
epoch_loss = 0.0
for b in range(num_batch):
ConvolutionLayer(in_channels=in_channels,
out_channels=4,
kernel_size=3,
stride=1,
pad=1))
CNN.add_layer('ReLU-1', ReLU())
CNN.add_layer(
'Conv-2',
ConvolutionLayer(in_channels=4,
out_channels=4,
kernel_size=3,
stride=1,
pad=1))
CNN.add_layer('ReLU-2', ReLU())
CNN.add_layer('Max-pool - 1', MaxPoolingLayer(kernel_size=2, stride=2))
CNN.add_layer('FC-1', FCLayer(input_dim=784, output_dim=500))
CNN.add_layer('ReLU-3', ReLU())
CNN.add_layer('FC-2', FCLayer(input_dim=500, output_dim=10))
CNN.add_layer('Softmax Layer', SoftmaxLayer())
# =========================================================================
CNN.summary()
print('Training Starts...')
num_batch = int(np.ceil(num_train / batch_size))
for epoch in range(1, num_epochs + 1):
start = time.time()
epoch_loss = 0.0
for b in range(num_batch):
s = b * batch_size
