Add names to TensorFlow model

model-5conv_3fc.py

@@ -16,7 +16,7 @@ def bias_variable(shape):
 def conv2d(x, W, stride):
     return tf.nn.conv2d(x, W, strides=[1, stride, stride, 1], padding='VALID')
 
-x = tf.placeholder(tf.float32, shape=[None, params.img_height, params.img_width, params.img_channels])
+x = tf.placeholder(tf.float32, shape=[None, params.img_height, params.img_width, params.img_channels], name="input_x")
 y_ = tf.placeholder(tf.float32, shape=[None, 1])
 
 x_image = x
@@ -25,53 +25,53 @@ def conv2d(x, W, stride):
 W_conv1 = weight_variable("wc1", [5, 5, 3, 24])
 b_conv1 = bias_variable([24])
 
-h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1, 2) + b_conv1)
+h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1, 2) + b_conv1, name="relu1")
 
 # second convolutional layer
 W_conv2 = weight_variable("wc2", [5, 5, 24, 36])
 b_conv2 = bias_variable([36])
 
-h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2, 2) + b_conv2)
+h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2, 2) + b_conv2, name="relu2")
 
 # third convolutional layer
 W_conv3 = weight_variable("wc3", [5, 5, 36, 48])
 b_conv3 = bias_variable([48])
 
-h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv3, 2) + b_conv3)
+h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv3, 2) + b_conv3, name="relu3")
 
 # fourth convolutional layer
 W_conv4 = weight_variable("wc4", [3, 3, 48, 64])
 b_conv4 = bias_variable([64])
 
-h_conv4 = tf.nn.relu(conv2d(h_conv3, W_conv4, 1) + b_conv4)
+h_conv4 = tf.nn.relu(conv2d(h_conv3, W_conv4, 1) + b_conv4, name="relu4")
 
 # fifth convolutional layer
 W_conv5 = weight_variable("wc5", [3, 3, 64, 64])
 b_conv5 = bias_variable([64])
 
-h_conv5 = tf.nn.relu(conv2d(h_conv4, W_conv5, 1) + b_conv5)
-h_conv5_flat = tf.reshape(h_conv5, [-1, 1152])
+h_conv5 = tf.nn.relu(conv2d(h_conv4, W_conv5, 1) + b_conv5, name="relu5")
+h_conv5_flat = tf.reshape(h_conv5, [-1, 1152], name="reshape1")
 
 # fully connected layer 2
 W_fc2 = weight_variable("fc2", [1152, 100])
 b_fc2 = bias_variable([100])
 
-h_fc2 = tf.nn.relu(tf.matmul(h_conv5_flat, W_fc2) + b_fc2)
+h_fc2 = tf.nn.relu(tf.matmul(h_conv5_flat, W_fc2, name="matmul1") + b_fc2, name="relu6")
 
 # fully connected layer 3
 W_fc3 = weight_variable("fc3", [100, 50])
 b_fc3 = bias_variable([50])
 
-h_fc3 = tf.nn.relu(tf.matmul(h_fc2, W_fc3) + b_fc3)
+h_fc3 = tf.nn.relu(tf.matmul(h_fc2, W_fc3, name="matmul2") + b_fc3, name="relu7")
 
 # fully connected layer 4
 W_fc4 = weight_variable("fc4", [50, 10])
 b_fc4 = bias_variable([10])
 
-h_fc4 = tf.nn.relu(tf.matmul(h_fc3, W_fc4) + b_fc4)
+h_fc4 = tf.nn.relu(tf.matmul(h_fc3, W_fc4, name="matmul3") + b_fc4, name="relu8")
 
 # output
 W_fc5 = weight_variable("fc5", [10, 1])
 b_fc5 = bias_variable([1])
 
-y = tf.multiply(tf.atan(tf.matmul(h_fc4, W_fc5) + b_fc5), 2) #scale the atan output
+y = tf.multiply(tf.atan(tf.matmul(h_fc4, W_fc5, name="matmul4") + b_fc5), 2, name="atan1") #scale the atan output