def classifier(x, dropout): pool5 = cnn(x) dim = pool5.get_shape().as_list() flat_dim = dim[1] * dim[2] * dim[3] flat = tf.reshape(pool5, [-1, flat_dim]) with tf.name_scope('classifier') as scope: with tf.name_scope('classifier_fc1') 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.relu(fc1) fc1 = tf.nn.dropout(fc1, dropout) with tf.name_scope('classifier_fc2') 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.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 cnn(x): with tf.name_scope('cnn') as scope: with tf.name_scope('cnn_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.relu(conv1) norm1 = tu.lrn(conv1, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75) pool1 = tu.max_pool2d(norm1, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') with tf.name_scope('cnn_conv2') as inner_scope: wcnn2 = tu.weight([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.relu(conv2) norm2 = tu.lrn(conv2, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75) pool2 = tu.max_pool2d(norm2, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') with tf.name_scope('cnn_conv3') as inner_scope: wcnn3 = tu.weight([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.relu(conv3) with tf.name_scope('cnn_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, wcnn4, stride=(1, 1), padding='SAME'), bcnn4) conv4 = tu.relu(conv4) with tf.name_scope('cnn_conv5') as inner_scope: wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5') bcnn5 = tu.bias(1.0, [256], name='bcnn5') conv5 = tf.add( tu.conv2d(conv4, wcnn5, stride=(1, 1), padding='SAME'), bcnn5) conv5 = tu.relu(conv5) pool5 = tu.max_pool2d(conv5, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') return pool5
def cnn(x): """ AlexNet convolutional 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('alexnet_cnn') as scope: with tf.name_scope('alexnet_cnn_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=2, bias=1.0, alpha=2e-05, beta=0.75) pool1 = tu.max_pool2d(norm1, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') with tf.name_scope('alexnet_cnn_conv2') as inner_scope: wcnn2 = tu.weight([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=2, bias=1.0, alpha=2e-05, beta=0.75) pool2 = tu.max_pool2d(norm2, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') with tf.name_scope('alexnet_cnn_conv3') as inner_scope: wcnn3 = tu.weight([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('alexnet_cnn_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, wcnn4, stride=(1, 1), padding='SAME'), bcnn4) # conv4 = tu.batch_norm(conv4) conv4 = tu.relu(conv4) with tf.name_scope('alexnet_cnn_conv5') as inner_scope: wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5') bcnn5 = tu.bias(1.0, [256], name='bcnn5') conv5 = tf.add(tu.conv2d(conv4, 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
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 = cnn(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('alexnet_classifier') as scope: with tf.name_scope('alexnet_classifier_fc1') as inner_scope: wfc1 = tu.weight([flat_dim, 4096], name='wfc1') wfc_1 = tu.weight([flat_dim, 4096], name='wfc_1') bfc1 = tu.bias(0.0, [4096], name='bfc1') alpha_full_1 = compute_alpha(wfc1) wfc_1 = tenary_opration(wfc1) flat = tf.multiply(flat, alpha_full_1) fc1 = tf.add(tf.matmul(flat, wfc_1), bfc1) fc1 = tu.batch_norm(fc1) fc1 = selu(fc1) fc1 = tf.nn.dropout(fc1, dropout) with tf.name_scope('alexnet_classifier_fc2') as inner_scope: wfc2 = tu.weight([4096, 4096], name='wfc2') wfc_2 = tu.weight([4096, 4096], name='wfc_2') bfc2 = tu.bias(0.0, [4096], name='bfc2') alpha6 = compute_alpha(wfc2) wfc_2 = tenary_opration(wfc2) fc1 = tf.multiply(fc1, alpha6) fc2 = tf.add(tf.matmul(fc1, wfc_2), bfc2) fc2 = tu.batch_norm(fc2) fc2 = selu(fc2) fc2 = tf.nn.dropout(fc2, dropout) with tf.name_scope('alexnet_classifier_output') as inner_scope: wfc3 = tu.weight([4096, 1000], name='wfc3') bfc3 = tu.bias(0.0, [1000], name='bfc3') # wfc3 = tenary_opration(wfc3) fc3 = tf.add(tf.matmul(fc2, wfc3), bfc3) softmax = tf.nn.softmax(fc3) return fc3, softmax
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 = cnn(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('alexnet_classifier') as scope: with tf.name_scope('alexnet_classifier_fc1') 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('alexnet_classifier_fc2') 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('alexnet_classifier_output') as inner_scope: wfc3 = tu.weight([4096, 6], name='wfc3') bfc3 = tu.bias(0.0, [6], name='bfc3') fc3 = tf.add(tf.matmul(fc2, wfc3), bfc3) softmax = tf.nn.softmax(fc3) return fc3, softmax
def cnn(x): """ AlexNet convolutional 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('alexnet_cnn') as scope: with tf.name_scope('alexnet_cnn_conv1') as inner_scope: wcnn1 = tu.weight([11, 11, 3, 96], name='wcnn1') # bcnn1 = tu.bias(0.0, [96], name='bcnn1') # wcnn1_t = fw(wcnn1) # x_t =fa(cabs(x)) conv1 = tu.conv2d(x, wcnn1, stride=(4, 4), padding='SAME') #conv1 = tu.batch_norm(conv1) conv1 = tf.nn.relu(conv1) norm1 = tu.lrn(conv1, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75) pool1 = tu.max_pool2d(norm1, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') with tf.name_scope('alexnet_cnn_conv2') as inner_scope: wcnn2 = tu.weight([5, 5, 96, 256], name='wcnn2') # bcnn2 = tu.bias(1.0, [256], name='bcnn2') pool1_t = fa(cabs(pool1)) wcnn2_t = fw(wcnn2) conv2 = tu.conv2d(pool1_t, wcnn2_t, stride=(1, 1), padding='SAME') #conv2 = tu.batch_norm(conv2) conv2 = tf.nn.relu(conv2) norm2 = tu.lrn(conv2, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75) pool2 = tu.max_pool2d(norm2, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') with tf.name_scope('alexnet_cnn_conv3') as inner_scope: wcnn3 = tu.weight([3, 3, 256, 384], name='wcnn3') # bcnn3 = tu.bias(0.0, [384], name='bcnn3') pool2_t = fa(cabs(pool2)) wcnn3_t = fw(wcnn3) conv3 = tu.conv2d(pool2_t, wcnn3_t, stride=(1, 1), padding='SAME') #conv3 = tu.batch_norm(conv3) conv3 = tf.nn.relu(conv3) with tf.name_scope('alexnet_cnn_conv4') as inner_scope: wcnn4 = tu.weight([3, 3, 384, 384], name='wcnn4') # bcnn4 = tu.bias(1.0, [384], name='bcnn4') conv3_t = fa(cabs(conv3)) wcnn4_t = fw(wcnn4) conv4 = tu.conv2d(conv3_t, wcnn4_t, stride=(1, 1), padding='SAME') #conv4 = tu.batch_norm(conv4) conv4 = tf.nn.relu(conv4) with tf.name_scope('alexnet_cnn_conv5') as inner_scope: wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5') # bcnn5 = tu.bias(1.0, [256], name='bcnn5') conv4_t = fa(cabs(conv4)) wcnn5_t = fw(wcnn5) conv5 = tu.conv2d(conv4_t, wcnn5_t, stride=(1, 1), padding='SAME') #conv5 = tu.batch_norm(conv5) conv5 = tf.nn.relu(conv5) pool5 = tu.max_pool2d(conv5, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') return pool5
def cnn(x): """ AlexNet convolutional 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('alexnet_cnn') as scope: with tf.name_scope('alexnet_cnn_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.batch_norm(conv1) pool1 = tu.max_pool2d(norm1, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') with tf.name_scope('alexnet_cnn_conv2') as inner_scope: wcnn2 = tu.weight([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.batch_norm(conv2) pool2 = tu.max_pool2d(norm2, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') with tf.name_scope('alexnet_cnn_conv3') as inner_scope: wcnn3 = tu.weight([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('alexnet_cnn_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, wcnn4, stride=(1, 1), padding='SAME'), bcnn4) conv4 = tu.batch_norm(conv4) conv4 = tu.relu(conv4) with tf.name_scope('alexnet_cnn_conv5') as inner_scope: wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5') bcnn5 = tu.bias(1.0, [256], name='bcnn5') conv5 = tf.add( tu.conv2d(conv4, 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
def cnn(x): """ AlexNet convolutional 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('alexnet_cnn') as scope: with tf.name_scope('alexnet_cnn_conv1') as inner_scope: wcnn1 = tu.weight([11, 11, 3, 96], name='wcnn1') bcnn1 = tu.bias(0.0, [96], name='bcnn1') # alpha1 = compute_alpha(wcnn1) # wcnn1 = tenary_opration(wcnn1) # wcnn1_1 = tf.multiply(alpha1, wcnn1) conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(4, 4), padding='SAME'), bcnn1) conv1 = tu.batch_norm(conv1) conv1 = selu(conv1) # norm1 = tu.lrn(conv1, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75) pool1 = tu.max_pool2d(conv1, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') with tf.name_scope('alexnet_cnn_conv2') as inner_scope: wcnn2 = tu.weight([5, 5, 96, 256], name='wcnn2') wcnn_2 = tu.weight([5, 5, 96, 256], name='wcnn_2') bcnn2 = tu.bias(1.0, [256], name='bcnn2') alpha2 = compute_alpha(wcnn2) pool1 = tf.multiply(pool1, alpha2) wcnn_2 = tenary_opration(wcnn2) # wcnn_2 = tf.multiply(alpha2, wcnn2) conv2 = tf.add( tu.conv2d(pool1, wcnn_2, stride=(1, 1), padding='SAME'), bcnn2) conv2 = tu.batch_norm(conv2) conv2 = selu(conv2) # norm2 = tu.lrn(conv2, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75) pool2 = tu.max_pool2d(conv2, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') with tf.name_scope('alexnet_cnn_conv3') as inner_scope: wcnn3 = tu.weight([3, 3, 256, 384], name='wcnn3') wcnn_3 = tu.weight([3, 3, 256, 384], name='wcnn_3') bcnn3 = tu.bias(0.0, [384], name='bcnn3') alpha3 = compute_alpha(wcnn3) wcnn_3 = tenary_opration(wcnn3) pool2 = tf.multiply(pool2, alpha3) conv3 = tf.add( tu.conv2d(pool2, wcnn_3, stride=(1, 1), padding='SAME'), bcnn3) conv3 = tu.batch_norm(conv3) conv3 = selu(conv3) with tf.name_scope('alexnet_cnn_conv4') as inner_scope: wcnn4 = tu.weight([3, 3, 384, 384], name='wcnn4') wcnn_4 = tu.weight([3, 3, 383, 384], name='wcnn_4') bcnn4 = tu.bias(1.0, [384], name='bcnn4') alpha4 = compute_alpha(wcnn4) wcnn_4 = tenary_opration(wcnn4) conv3 = tf.multiply(conv3, alpha4) conv4 = tf.add( tu.conv2d(conv3, wcnn_4, stride=(1, 1), padding='SAME'), bcnn4) conv4 = tu.batch_norm(conv4) conv4 = selu(conv4) with tf.name_scope('alexnet_cnn_conv5') as inner_scope: wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5') wcnn_5 = tu.weight([3, 3, 384, 256], name='wcnn_5') bcnn5 = tu.bias(1.0, [256], name='bcnn5') alpha5 = compute_alpha(wcnn5) wcnn_5 = tenary_opration(wcnn5) conv4 = tf.multiply(conv4, alpha5) conv5 = tf.add( tu.conv2d(conv4, wcnn_5, stride=(1, 1), padding='SAME'), bcnn5) conv5 = tu.batch_norm(conv5) conv5 = selu(conv5) pool5 = tu.max_pool2d(conv5, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID') return pool5
def cnn(x): """ AlexNet convolutional 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('vgg_cnn') as scope: with tf.name_scope('vgg_cnn_conv1') as inner_scope: wcnn1 = tu.weight([3, 3, 3, 64], name='wcnn1') bcnn1 = tu.bias(0.0, [64], name='bcnn1') conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(1,1), padding='SAME'), bcnn1) conv1 = tu.relu(conv1) with tf.name_scope('vgg_cnn_conv2') as inner_scope: wcnn2 = tu.weight([3, 3, 64, 64], name='wcnn2') bcnn2 = tu.bias(0.0, [64], name='bcnn2') conv2 = tf.add(tu.conv2d(conv1, wcnn2, stride=(1,1),padding='SAME'), bcnn2) conv2 = tu.relu(conv2) with tf.name_scope('vgg_cnn_pool1') as inner_scope: pool1 = tu.max_pool2d(conv2, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME') with tf.name_scope('vgg_cnn_conv3') as inner_scope: wcnn3 = tu.weight([3, 3, 64, 128], name = 'wcnn3') bcnn3 = tu.bias(0.0, [128], name = 'bcnn3') conv3 = tf.add(tu.conv2d(pool1, wcnn3, stride = (1,1), padding = 'SAME'), bcnn3) conv3 = tu.relu(conv3) with tf.name_scope('vgg_cnn_conv4') as inner_scope: wcnn4 = tu.weight([3, 3, 128, 128], name = 'wcnn4') bcnn4 = tu.bias(0.0, [128], name = 'bcnn4') conv4 = tf.add(tu.conv2d(conv3, wcnn4, stride = (1,1), padding = 'SAME'), bcnn4) conv4 = tu.relu(conv4) with tf.name_scope('vgg_cnn_pool2') as inner_scope: pool2 = tu.max_pool2d(conv4, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME') with tf.name_scope('vgg_cnn_conv5') as inner_scope: wcnn5 = tu.weight([3, 3, 128, 256 ], name='wcnn5') bcnn5 = tu.bias(0.0, [256], name='bcnn5') conv5 = tf.add(tu.conv2d(pool2, wcnn5, stride=(1,1), padding='SAME'), bcnn5) conv5 = tu.relu(conv5) with tf.name_scope('vgg_cnn_conv6') as inner_scope: wcnn6 = tu.weight([3, 3, 256, 256], name='wcnn6') bcnn6 = tu.bias(0.0, [256], name='bcnn6') conv6 = tf.add(tu.conv2d(conv5, wcnn6, stride=(1,1), padding='SAME'), bcnn5) conv6 = tu.relu(conv6) with tf.name_scope('vgg_cnn_conv7') as inner_scope: wcnn7 = tu.weight([3, 3, 256, 256], name='wcnn7') bcnn7 = tu.bias(0.0, [256], name='bcnn7') conv7 = tf.add(tu.conv2d(conv6, wcnn7, stride=(1,1), padding='SAME'), bcnn7) conv7 = tu.relu(conv7) with tf.name_scope('vgg_cnn_conv8') as inner_scope: wcnn8 = tu.weight([3, 3, 256, 256], name='wcnn8') bcnn8 = tu.bias(0.0, [256], name='bcnn8') conv8 = tf.add(tu.conv2d(conv7, wcnn8, stride=(1,1), padding='SAME'), bcnn8) conv8 = tu.relu(conv8) with tf.name_scope('vgg_cnn_pool3') as inner_scope: pool3 = tu.max_pool2d(conv8, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME') with tf.name_scope('vgg_cnn_conv9') as inner_scope: wcnn9 = tu.weight([3, 3, 256, 512], name='wcnn9') bcnn9 = tu.bias(0.0, [512], name='bcnn9') conv9 = tf.add(tu.conv2d(pool3, wcnn9, stride=(1,1), padding='SAME'), bcnn9) conv9 = tu.relu(conv9) with tf.name_scope('vgg_cnn_conv10') as inner_scope: wcnn10 = tu.weight([3, 3, 512, 512], name='wcnn10') bcnn10 = tu.bias(0.0, [512], name='bcnn10') conv10 = tf.add(tu.conv2d(conv9, wcnn10, stride=(1,1), padding='SAME'), bcnn10) conv10 = tu.relu(conv10) with tf.name_scope('vgg_cnn_conv11') as inner_scope: wcnn11 = tu.weight([3, 3, 512, 512], name='wcnn11') bcnn11 = tu.bias(0.0, [512], name='bcnn11') conv11 = tf.add(tu.conv2d(conv10, wcnn11, stride=(1,1), padding='SAME'), bcnn11) conv11 = tu.relu(conv11) with tf.name_scope('vgg_cnn_conv12') as inner_scope: wcnn12 = tu.weight([3, 3, 512, 512], name='wcnn12') bcnn12 = tu.bias(0.0, [512], name='bcnn12') conv12 = tf.add(tu.conv2d(conv11, wcnn12, stride=(1,1), padding='SAME'), bcnn12) conv12 = tu.relu(conv12) with tf.name_scope('vgg_cnn_pool4') as inner_scope: pool4 = tu.max_pool2d(conv12, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME') with tf.name_scope('vgg_cnn_conv13') as inner_scope: wcnn13 = tu.weight([3, 3, 512, 512], name='wcnn13') bcnn13 = tu.bias(0.0, [512], name='bcnn13') conv13 = tf.add(tu.conv2d(pool4, wcnn13, stride=(1,1), padding='SAME'), bcnn13) conv13 = tu.relu(conv13) with tf.name_scope('vgg_cnn_conv14') as inner_scope: wcnn14 = tu.weight([3, 3, 512, 512], name='wcnn14') bcnn14 = tu.bias(0.0, [512], name='bcnn14') conv14 = tf.add(tu.conv2d(conv13, wcnn14, stride=(1,1), padding='SAME'), bcnn14) conv14 = tu.relu(conv14) with tf.name_scope('vgg_cnn_conv15') as inner_scope: wcnn15 = tu.weight([3, 3, 512, 512], name='wcnn15') bcnn15 = tu.bias(0.0, [512], name='bcnn15') conv15 = tf.add(tu.conv2d(conv14, wcnn15, stride=(1,1), padding='SAME'), bcnn15) conv15 = tu.relu(conv15) with tf.name_scope('vgg_cnn_conv16') as inner_scope: wcnn16 = tu.weight([3, 3, 512, 512], name='wcnn16') bcnn16 = tu.bias(0.0, [512], name='bcnn16') conv16 = tf.add(tu.conv2d(conv15, wcnn16, stride=(1,1), padding='SAME'), bcnn16) conv16 = tu.relu(conv16) with tf.name_scope('vgg_cnn_pool5') as inner_scope: pool5 = tu.max_pool2d(conv16, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME') return pool5