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, 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 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