def cnn(x):
""" CNN model to detect lung cancer
Args:
x: tensor of shape [batch_size, width, height, channels]
Returns:
pool2: tensor with all convolutions, pooling applied
"""
with tf.name_scope('cnn') as scope:
with tf.name_scope('conv1') as inner_scope:
wcnn1 = tu.weight([3, 3, 1, 64], name='wcnn1')
bcnn1 = tu.bias(1.0, [64], name='bcnn1')
conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(1, 1), padding='SAME'), bcnn1)
conv1 = tu.relu(conv1)
# (?, 192, 192, 64)
with tf.name_scope('conv2') as inner_scope:
wcnn2 = tu.weight([3, 3, 64, 64], name='wcnn2')
bcnn2 = tu.bias(1.0, [64], name='bcnn2')
conv2 = tf.add(tu.conv2d(conv1, wcnn2, stride=(1, 1), padding='SAME'), bcnn2)
conv2 = tu.relu(conv2)
#(?, 192, 192, 64)
with tf.name_scope('max_pool') as inner_scope:
pool1 = tu.max_pool2d(conv2, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME')
# (?, 96, 96, 64)
with tf.name_scope('conv3') as inner_scope:
wcnn3 = tu.weight([3, 3, 64, 64], name='wcnn3')
bcnn3 = tu.bias(1.0, [64], name='bcnn3')
conv3 = tf.add(tu.conv2d(pool1, wcnn3, stride=(1, 1), padding='SAME'), bcnn3)
conv3 = tu.relu(conv3)
# (?, 96, 96, 64)
with tf.name_scope('conv4') as inner_scope:
wcnn4 = tu.weight([3, 3, 64, 64], name='wcnn4')
bcnn4 = tu.bias(1.0, [64], name='bcnn4')
conv4 = tf.add(tu.conv2d(conv3, wcnn4, stride=(1, 1), padding='SAME'), bcnn4)
conv4 = tu.relu(conv4)
# (?, 96, 96, 64)
with tf.name_scope('conv5') as inner_scope:
wcnn5 = tu.weight([3, 3, 64, 64], name='wcnn5')
bcnn5 = tu.bias(1.0, [64], name='bcnn5')
conv5 = tf.add(tu.conv2d(conv4, wcnn5, stride=(1, 1), padding='SAME'), bcnn5)
conv5 = tu.relu(conv5)
# (?, 96, 96, 64)
with tf.name_scope('max_pool') as inner_scope:
pool2 = tu.max_pool2d(conv5, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME')
# (?, 48, 48, 64)
return pool2
def classifier(x):
#parameters for convolution kernels
IMG_DEPTH = 1
C1_KERNEL_SIZE, C2_KERNEL_SIZE, C3_KERNEL_SIZE = 5, 5, 5
C1_OUT_CHANNELS, C2_OUT_CHANNELS, C3_OUT_CHANNELS = 6, 16, 120
C1_STRIDES, C2_STRIDES, C3_STRIDES = 1, 1, 1
P1_SIZE, P2_SIZE = 2, 2
P1_STRIDE, P2_STRIDE = 2, 2
F4_SIZE, F5_SIZE = 84, 10
C1_kernel = util.weights(
[C1_KERNEL_SIZE, C1_KERNEL_SIZE, IMG_DEPTH, C1_OUT_CHANNELS], 0.1,
'C1_kernel')
C2_kernel = util.weights(
[C2_KERNEL_SIZE, C2_KERNEL_SIZE, C1_OUT_CHANNELS, C2_OUT_CHANNELS],
0.1, 'C2_kernel')
C3_kernel = util.weights(
[C3_KERNEL_SIZE, C3_KERNEL_SIZE, C2_OUT_CHANNELS, C3_OUT_CHANNELS],
0.1, 'C3_kernel')
C1_bias = util.bias([C1_OUT_CHANNELS], 'C1_bias')
C2_bias = util.bias([C2_OUT_CHANNELS], 'C2_bias')
C3_bias = util.bias([C3_OUT_CHANNELS], 'C3_bias')
#LeNet-5 structure
C1 = util.convLayer(x, C1_kernel, C1_STRIDES, 'SAME')
ReLU1 = tf.nn.relu(C1 + C1_bias)
P1 = util.max_pool(ReLU1, P1_SIZE, P1_STRIDE)
C2 = util.convLayer(P1, C2_kernel, C2_STRIDES, 'SAME')
ReLU2 = tf.nn.relu(C2 + C2_bias)
P2 = util.max_pool(ReLU2, P2_SIZE, P2_STRIDE)
C3 = util.convLayer(P2, C3_kernel, C3_STRIDES, 'SAME')
ReLU3 = tf.nn.relu(C3 + C3_bias)
num_F4_in = (int)(ReLU3.shape[1] * ReLU3.shape[2] * ReLU3.shape[3])
F4_in = tf.reshape(ReLU3, [-1, num_F4_in])
F4_weights = util.weights([num_F4_in, F4_SIZE], 0.1, 'F4_weights')
F4_bias = util.bias([F4_SIZE], 'F4_bias')
F4 = tf.matmul(F4_in, F4_weights)
ReLU4 = tf.nn.relu(F4 + F4_bias)
F5_weights = util.weights([F4_SIZE, F5_SIZE], 0.1, 'F5_weights')
F5_bias = util.bias([F5_SIZE], 'F5_bias')
F5 = tf.matmul(ReLU4, F5_weights) + F5_bias
return F5
def classifier(x):
#Initialize parameters in nn
F1_weights = util.weights([784, 784], 0.01, 'F1_weights')
F1_bias = util.bias([784], 'F1_bias')
F2_weights = util.weights([784, 10], 0.01, 'F2_weights')
F2_bias = util.bias([10], 'F2_bias')
F1 = tf.matmul(x, F1_weights) + F1_bias
ReLU1 = tf.nn.relu(F1)
F2 = tf.matmul(ReLU1, F2_weights) + F2_bias
y = tf.nn.softmax(F2)
return y
def classifier(x, dropout):
"""
AlexNet fully connected layers definition
Args:
x: tensor of shape [batch_size, width, height, channels]
dropout: probability of non dropping out units
Returns:
fc3: 1000 linear tensor taken just before applying the softmax operation
it is needed to feed it to tf.softmax_cross_entropy_with_logits()
softmax: 1000 linear tensor representing the output probabilities of the image to classify
"""
pool5 = alexnet(x)
dim = pool5.get_shape().as_list()
flat_dim = dim[1] * dim[2] * dim[3] # 6 * 6 * 256
flat = tf.reshape(pool5, [-1, flat_dim])
with tf.name_scope('classifier') as scope:
with tf.name_scope('fullyconected1') as inner_scope:
wfc1 = tu.weight([flat_dim, 4096], name='wfc1')
bfc1 = tu.bias(0.0, [4096], name='bfc1')
fc1 = tf.add(tf.matmul(flat, wfc1), bfc1)
#fc1 = tu.batch_norm(fc1)
fc1 = tu.relu(fc1)
fc1 = tf.nn.dropout(fc1, dropout)
with tf.name_scope('fullyconected2') as inner_scope:
wfc2 = tu.weight([4096, 4096], name='wfc2')
bfc2 = tu.bias(0.0, [4096], name='bfc2')
fc2 = tf.add(tf.matmul(fc1, wfc2), bfc2)
#fc2 = tu.batch_norm(fc2)
fc2 = tu.relu(fc2)
fc2 = tf.nn.dropout(fc2, dropout)
with tf.name_scope('classifier_output') as inner_scope:
wfc3 = tu.weight([4096, 1000], name='wfc3')
bfc3 = tu.bias(0.0, [1000], name='bfc3')
fc3 = tf.add(tf.matmul(fc2, wfc3), bfc3)
softmax = tf.nn.softmax(fc3)
return fc3, softmax
def classifier(x, dropout):
"""cnn fully connected layers definition
Args:
x: tensor of shape [batch_size, width, height, channels]
dropout: probability of non dropping out units
Returns:
fc3: 2 linear tensor taken just before applying the softmax operation
it is needed to feed it to tf.softmax_cross_entropy_with_logits()
softmax: 2 linear tensor representing the output probabilities of the image to classify
"""
pool2 = cnn(x)
dim = pool2.get_shape().as_list()
flat_dim = dim[1] * dim[2] * dim[3] # 48 * 48 * 64
flat = tf.reshape(pool2, [-1, flat_dim])
with tf.name_scope('classifier') as scope:
with tf.name_scope('fullyconected1') as inner_scope:
wfc1 = tu.weight([flat_dim, 500], name='wfc1')
bfc1 = tu.bias(1.0, [500], name='bfc1')
fc1 = tf.add(tf.matmul(flat, wfc1), bfc1)
fc1 = tu.relu(fc1)
fc1 = tf.nn.dropout(fc1, dropout)
with tf.name_scope('fullyconected2') as inner_scope:
wfc2 = tu.weight([500, 100], name='wfc2')
bfc2 = tu.bias(1.0, [100], name='bfc2')
fc2 = tf.add(tf.matmul(fc1, wfc2), bfc2)
fc2 = tu.relu(fc2)
fc2 = tf.nn.dropout(fc2, dropout)
with tf.name_scope('classifier_output') as inner_scope:
wfc3 = tu.weight([100, 2], name='wfc3')
bfc3 = tu.bias(1.0, [2], name='bfc3')
fc3 = tf.add(tf.matmul(fc2, wfc3), bfc3)
softmax = tf.nn.softmax(fc3)
return fc3, softmax
def cnn(x):
""" CNN model to detect lung cancer
Args:
x: tensor of shape [batch_size, width, height, channels]
Returns:
pool2: tensor with all convolutions, pooling applied
"""
with tf.name_scope('cnn') as scope:
with tf.name_scope('conv1') as inner_scope:
wcnn1 = tu.weight([3, 3, 1, 64], name='wcnn1')
bcnn1 = tu.bias(1.0, [64], name='bcnn1')
conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(1, 1), padding='SAME'),
bcnn1)
conv1 = tu.relu(conv1)
# (?, 192, 192, 64)
with tf.name_scope('conv2') as inner_scope:
wcnn2 = tu.weight([3, 3, 64, 64], name='wcnn2')
bcnn2 = tu.bias(1.0, [64], name='bcnn2')
conv2 = tf.add(
tu.conv2d(conv1, wcnn2, stride=(1, 1), padding='SAME'), bcnn2)
conv2 = tu.relu(conv2)
#(?, 192, 192, 64)
with tf.name_scope('max_pool') as inner_scope:
pool1 = tu.max_pool2d(conv2,
kernel=[1, 2, 2, 1],
stride=[1, 2, 2, 1],
padding='SAME')
# (?, 96, 96, 64)
with tf.name_scope('conv3') as inner_scope:
wcnn3 = tu.weight([3, 3, 64, 64], name='wcnn3')
bcnn3 = tu.bias(1.0, [64], name='bcnn3')
conv3 = tf.add(
tu.conv2d(pool1, wcnn3, stride=(1, 1), padding='SAME'), bcnn3)
conv3 = tu.relu(conv3)
# (?, 96, 96, 64)
with tf.name_scope('conv4') as inner_scope:
wcnn4 = tu.weight([3, 3, 64, 64], name='wcnn4')
bcnn4 = tu.bias(1.0, [64], name='bcnn4')
conv4 = tf.add(
tu.conv2d(conv3, wcnn4, stride=(1, 1), padding='SAME'), bcnn4)
conv4 = tu.relu(conv4)
# (?, 96, 96, 64)
with tf.name_scope('conv5') as inner_scope:
wcnn5 = tu.weight([3, 3, 64, 64], name='wcnn5')
bcnn5 = tu.bias(1.0, [64], name='bcnn5')
conv5 = tf.add(
tu.conv2d(conv4, wcnn5, stride=(1, 1), padding='SAME'), bcnn5)
conv5 = tu.relu(conv5)
# (?, 96, 96, 64)
with tf.name_scope('max_pool') as inner_scope:
pool2 = tu.max_pool2d(conv5,
kernel=[1, 2, 2, 1],
stride=[1, 2, 2, 1],
padding='SAME')
# (?, 48, 48, 64)
return pool2
def classifier(x):
#parameters for convolution kernels
IMG_DEPTH = 1
C1_KERNEL_SIZE, C2_KERNEL_SIZE, C3_KERNEL_SIZE, C4_KERNEL_SIZE, C5_KERNEL_SIZE = 11, 5, 3, 3, 3
C1_OUT_CHANNELS, C2_OUT_CHANNELS, C3_OUT_CHANNELS, C4_OUT_CHANNELS, C5_OUT_CHANNELS = 96, 256, 384, 384, 256
#C1_OUT_CHANNELS,C2_OUT_CHANNELS,C3_OUT_CHANNELS,C4_OUT_CHANNELS,C5_OUT_CHANNELS=48,128,197,197,128
C1_STRIDES, C2_STRIDES, C3_STRIDES, C4_STRIDES, C5_STRIDES = 1, 1, 1, 1, 1
P1_SIZE, P2_SIZE, P5_SIZE = 3, 3, 3
P1_STRIDE, P2_STRIDE, P5_STRIDE = 2, 2, 2
F6_SIZE, F7_SIZE = 4096, 4096
F8_SIZE = 10
#convolution kernels and bias
C1_kernel = util.weights(
[C1_KERNEL_SIZE, C1_KERNEL_SIZE, IMG_DEPTH, C1_OUT_CHANNELS], 0.01,
'C1_kernel')
C2_kernel = util.weights(
[C2_KERNEL_SIZE, C2_KERNEL_SIZE, C1_OUT_CHANNELS, C2_OUT_CHANNELS],
0.01, 'C2_kernel')
C3_kernel = util.weights(
[C3_KERNEL_SIZE, C3_KERNEL_SIZE, C2_OUT_CHANNELS, C3_OUT_CHANNELS],
0.01, 'C3_kernel')
C4_kernel = util.weights(
[C4_KERNEL_SIZE, C4_KERNEL_SIZE, C3_OUT_CHANNELS, C4_OUT_CHANNELS],
0.01, 'C4_kernel')
C5_kernel = util.weights(
[C5_KERNEL_SIZE, C5_KERNEL_SIZE, C4_OUT_CHANNELS, C5_OUT_CHANNELS],
0.01, 'C5_kernel')
C1_bias = util.bias([C1_OUT_CHANNELS], 'C1_bias')
C2_bias = util.bias([C2_OUT_CHANNELS], 'C2_bias')
C3_bias = util.bias([C3_OUT_CHANNELS], 'C3_bias')
C4_bias = util.bias([C4_OUT_CHANNELS], 'C4_bias')
C5_bias = util.bias([C5_OUT_CHANNELS], 'C5_bias')
#AlexNet network
#Conv layer 1
C1 = util.convLayer(x, C1_kernel, C1_STRIDES, 'SAME')
ReLU1 = tf.nn.relu(C1 + C1_bias)
P1 = util.max_pool(ReLU1, P1_SIZE, P1_STRIDE)
NORM1 = tf.nn.local_response_normalization(P1)
#Conv layer 2
C2 = util.convLayer(NORM1, C2_kernel, C2_STRIDES, 'SAME')
ReLU2 = tf.nn.relu(C2 + C2_bias)
P2 = util.max_pool(ReLU2, P2_SIZE, P2_STRIDE)
NORM2 = tf.nn.local_response_normalization(P2)
#Conv layer 3
C3 = util.convLayer(NORM2, C3_kernel, C3_STRIDES, 'SAME')
ReLU3 = tf.nn.relu(C3 + C3_bias)
#Conv layer 4
C4 = util.convLayer(ReLU3, C4_kernel, C4_STRIDES, 'SAME')
ReLU4 = tf.nn.relu(C4 + C4_bias)
#Conv layer 5
C5 = util.convLayer(ReLU4, C5_kernel, C5_STRIDES, 'SAME')
ReLU5 = tf.nn.relu(C5 + C5_bias)
P5_pre = util.max_pool(ReLU5, P5_SIZE, P5_STRIDE)
num_P5_out = (int)(P5_pre.shape[1] * P5_pre.shape[2] * P5_pre.shape[3])
P5 = tf.reshape(P5_pre, [-1, num_P5_out])
#Fully connected layer 6
F6_weights = util.weights([num_P5_out, F6_SIZE], 0.01, 'F6_weights')
F6_bias = util.bias([F6_SIZE], 'F6_bias')
F6 = tf.matmul(P5, F6_weights)
ReLU6 = tf.nn.relu(F6 + F6_bias)
DROP6 = tf.nn.dropout(ReLU6, 0.5)
#Fully connected layer 7
F7_weights = util.weights([F6_SIZE, F7_SIZE], 0.01, 'F7_weights')
F7_bias = util.bias([F7_SIZE], 'F7_bias')
F7 = tf.matmul(DROP6, F7_weights)
ReLU7 = tf.nn.relu(F7 + F7_bias)
DROP7 = tf.nn.dropout(ReLU7, 0.5)
#Fully connected layer 8
F8_weights = util.weights([F7_SIZE, F8_SIZE], 0.01, 'F8_weights')
F8_bias = util.bias([F8_SIZE], 'F8_bias')
logits = tf.matmul(DROP7, F8_weights) + F8_bias
return logits
def alexnet(x):
"""
AlexNet conv layers definition
Args:
x: tensor of shape[batch_size,width,height,channels]
Returns:
pool5: tensor with all convolutions ,pooling and lrn operations applied
"""
with tf.name_scope('alexnetwork') as scope:
with tf.name_scope('conv1') as inner_scope:
wcnn1 = tu.weight([11, 11, 3, 96], name='wcnn1')
bcnn1 = tu.bias(0.0, [96], name='bcnn1')
conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(4, 4), padding='SAME'),
bcnn1)
#conv1 = tu.batch_norm(conv1)
conv1 = tu.relu(conv1)
norm1 = tu.lrn(conv1,
depth_radius=5,
bias=1.0,
alpha=1e-04,
beta=0.75)
pool1 = tu.max_pool2d(norm1,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
with tf.name_scope('conv2') as inner_scope:
wcnn2 = tu.weights([5, 5, 96, 256], name='wcnn2')
bcnn2 = tu.bias(1.0, [256], name='bcnn2')
conv2 = tf.add(
tu.conv2d(pool1, wcnn2, stride=(1, 1), padding='SAME'), bcnn2)
#conv2 = tu.batch_norm(conv2)
conv2 = tu.relu(conv2)
norm2 = tu.lrn(conv2,
depth_radius=5,
bias=1.0,
alpha=1e-04,
beta=0.75)
pool2 = tu.max_pool2d(norm2,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
with tf.name_scope('conv3') as inner_scope:
wcnn3 = tu.weights([3, 3, 256, 384], name='wcnn3')
bcnn3 = tu.bias(0.0, [384], name='bcnn3')
conv3 = tf.add(
tu.conv2d(pool2, wcnn3, stride=(1, 1), padding='SAME'), bcnn3)
#conv3 = tu.batch_norm(conv3)
conv3 = tu.relu(conv3)
with tf.name_scope('conv4') as inner_scope:
wcnn4 = tu.weight([3, 3, 384, 384], name='wcnn4')
bcnn4 = tu.bias(1.0, [384], name='bcnn4')
conv4 = tf.add(
tu.conv2d(conv3, wcnn5, stride=(1, 1), padding='SAME'), bcnn5)
#conv5 = tu.batch_norm(conv5)
conv5 = tu.relu(conv5)
pool5 = tu.max_pool2d(conv5,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
return pool5