我在将张量馈送到tensorflow中的图形时收到无效的参数错误？

Question

I am using tensorflow version 1.5 on Windows 10. I am using the Tensorflow slim model of Inception V4 network which has been picked up from the Website, using their pretrained weights and adding my own layers at the end to classify 120 different objects. This is the complete code except the lines containing the import modules and dataset paths.

image_size = 299
tf.logging.set_verbosity(tf.logging.INFO)
with slim.arg_scope(inception_blocks_v4.inception_v4_arg_scope()):
    X_input = tf.placeholder(tf.float32, shape = (None, image_size, image_size, 3))
    Y_label = tf.placeholder(tf.float32, shape = (None, num_classes))        
    targets = convert_to_onehot(labels_dir, no_of_features = num_classes)
    targets = tf.constant(targets, dtype = tf.float32)

    Images = [] 
    images = glob.glob(images_file_path)
    i = 0
    for my_img in images:
        image = mpimg.imread(my_img)[:, :, :3]
        image = tf.constant(image, dtype = tf.float32)
        Images.append(image)

    logits, end_points = inception_blocks_v4.inception_v4(inputs = X_input, num_classes = pre_num_classes, is_training = True, create_aux_logits= False)
    pretrained_weights = slim.assign_from_checkpoint_fn(ckpt_dir, slim.get_model_variables('InceptionV4'))
    with tf.Session() as sess:
        pretrained_weights(sess)

    my_layer = slim.fully_connected(logits, 560, activation_fn=tf.nn.relu, scope='myLayer1', weights_initializer = tf.truncated_normal_initializer(stddev = 0.001), weights_regularizer=slim.l2_regularizer(0.00005),biases_initializer = tf.truncated_normal_initializer(stddev=0.001), biases_regularizer=slim.l2_regularizer(0.00005))
    my_layer = slim.dropout(my_layer, keep_prob = 0.6, scope = 'myLayer2')
    my_layer = slim.fully_connected(my_layer, num_classes,activation_fn = tf.nn.relu,scope= 'myLayer3', weights_initializer = tf.truncated_normal_initializer(stddev=0.001), weights_regularizer=slim.l2_regularizer(0.00005), biases_initializer = tf.truncated_normal_initializer(stddev=0.001), biases_regularizer=slim.l2_regularizer(0.00005))
    my_layer_logits = slim.fully_connected(my_layer, num_classes, activation_fn=None,scope='myLayer4')  
    loss = tf.losses.softmax_cross_entropy(onehot_labels = Y_label, logits = my_layer_logits)  
    optimizer = tf.train.AdamOptimizer(learning_rate=0.0001)
    train_op = slim.learning.create_train_op(loss, optimizer) 
    images, labels = tf.train.batch([Images, targets], batch_size = 8, num_threads = 1, capacity = batch_size, enqueue_many=True)
    tensor_images = tf.convert_to_tensor(images, dtype = tf.float32)
    tensor_labels = tf.convert_to_tensor(labels, dtype = tf.float32)
    with tf.Session() as sess:
        print (tensor_images)
        print (tensor_labels)
    final_loss = slim.learning.train(train_op,logdir = new_ckpt_dir, number_of_steps = 1000, save_summaries_secs=5,log_every_n_steps=50)(feed_dict = {X_input:tensor_images ,Y_label: tensor_labels})  #{X_input:images ,Y_label: labels}

I have tried to pass the correct tensors of the data to the feed_dict of the graph during training operation step and having printed them giving me the following output.

Tensor("batch:0", shape=(8, 299, 299, 3), dtype=float32, device=/device:CPU:0)
Tensor("batch:1", shape=(8, 120), dtype=float32, device=/device:CPU:0)

But it also outputs the following error:

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,120]
 [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,120], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Answer 1

The correct way to feed data generated from tf.train.batch is like this:

Construct you model as following:

logits, end_points = inception_blocks_v4.inception_v4(
    inputs = tensor_images, num_classes = pre_num_classes, 
    is_training = True, create_aux_logits= False)

And in you loss, you should use:

loss = tf.losses.softmax_cross_entropy(
    onehot_labels = tf.one_hot(tensor_labels), logits = my_layer_logits)  

Feeding tensor into tf.placeholder is not supported currectly.

Note: I assume that your tensor_labels is just the indice of labels.