運行張量流模型時如何檢查每個張量的維數？

Question

我試過tf.Print的東西，但它似乎沒有打印任何東西。

這裏是我的代碼，我試圖tf.Print張量的維數。我應該如何修復代碼以打印出重塑的尺寸？

batch_size = 32 
image_height = 480 
image_width = 720 
num_channels = 4 
num_labels = 18 

deep_graph = tf.Graph() 
with deep_graph.as_default(): 

tf_valid_dataset = tf.constant(x_valid, dtype = tf.float32_ref) 
#tf_test_dataset = tf.constant(x_test, dtype = tf.float32_ref) 

tf_train_dataset = tf.placeholder(
    tf.float32, 
    shape = (batch_size, image_height, image_width, num_channels)) 

tf_train_labels = tf.placeholder(tf.float32, shape = (batch_size, num_labels)) 

conv1_weight = tf.Variable(
    tf.truncated_normal([5, 5, num_channels, 32], 
         stddev = 0.1, seed = 0)) 

conv1_biases = tf.Variable(tf.zeros([32])) 

conv2_weights = tf.Variable(
     tf.truncated_normal([5, 5, 32, 64], 
          stddev = 0.1, seed = 0, dtype = tf.float32)) 

conv2_biases = tf.Variable(tf.constant(0.1, shape = [64])) 

fc1_weights = tf.Variable(
    tf.truncated_normal([image_height * image_width * 32 , 512], 
         stddev = 0.1, seed = 0, dtype = tf.float32)) 

fc1_biases = tf.Variable(tf.constant(0.1, shape = [512])) 

fc2_weights = tf.Variable(
    tf.truncated_normal([512, 512], 
         stddev = 0.1, seed = 0, dtype = tf.float32)) 

fc2_biases = tf.Variable(tf.constant(0.1, shape = [512])) 

fc3_weights = tf.Variable(
    tf.truncated_normal([512, num_labels], stddev = 0.1, seed = 0, dtype = tf.float32)) 

fc3_biases = tf.Variable(tf.constant(0.1, shape = [num_labels])) 

def model(data, train = False): 
    conv1 = tf.nn.conv2d(data, conv1_weight, strides = [1,1,1,1], padding = 'SAME') 

    relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases)) 

    pool1 = tf.nn.max_pool(relu1, ksize = [1, 2, 2, 1], 
          strides = [1, 2, 2, 1], padding = 'SAME') 

    conv2 = tf.nn.conv2d(pool1, conv2_weights, strides = [1, 1, 1, 1], padding = 'SAME') 

    relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases)) 

    pool2 = tf.nn.max_pool(relu2, ksize = [1, 2, 2, 1], 
          strides = [1, 1, 1, 1], padding = 'SAME') 

    # Reshape the feature map cuboid into a 2D matrix to feed it to the 
    # fully connected layers. 
    pool_shape = pool2.get_shape().as_list() 
    reshape = tf.reshape(pool2, [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]]) 
    reshape = tf.Print(reshape, [reshape], "reshape: ", summarize=10) 
    # Fully connecte layers 
    hidden_layer1 = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases) 
    # For training data, add 0.5 dropout. 
    if train: 
     hidden_layer1 = tf.nn.dropout(hidden_layer1, 0.5, seed = 0) 

    hidden_layer2 = tf.nn.relu(tf.matmul(hidden_layer1, fc2_weights) + fc2_biases) 

    if train: 
     hidden_layer2 = tf.nn.dropout(hidden_layer2, 0.5, seed = 0) 

    return tf.matmul(hidden_layer2, fc3_weights) + fc3_biases 

# Call the model() function to make train_prediction 
train_prediction = model(tf_train_dataset, True) 

# calculate loss by using train_prediction 
loss = tf.reduce_mean(tf.reduce_sum(tf.square(train_prediction - tf_train_labels), 1)) 
# Add L2 regularization to loss 
loss += 1e-7 * (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) + 
     tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases) + 
     tf.nn.l2_loss(fc3_weights) + tf.nn.l2_loss(fc3_biases)) 

# Optimizer 
global_step = tf.Variable(0, name = 'global_step',trainable = False) 
starter_learning_rate = 0.001 
learning_rate = tf.train.exponential_decay(starter_learning_rate, 
              global_step, 
              100000, 
              0.96, 
              staircase = True) 

optimizer = tf.train.AdamOptimizer(learning_rate, 0.95).minimize(loss, global_step = global_step)  

Answer 1

你可以這樣做：

print (reshape.get_shape().as_list())

這將打印出您的張量清單的尺寸。如果您沒有as_list，則輸出將如下所示：TensorShape([Dimension(1), Dimension(2), Dimension(3)])