Python TensorFlow ValueError: Shape must be rank 1 but is rank 0

Question

I'm new on neural networks by Sentdex's tutorial. I tried to run that code:

   import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500

n_classes = 0
batch_size = 100

x = tf.placeholder('float',[None, 784])
y = tf.placeholder('float')
def neural_model(impuls):
    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([784, n_nodes_hl1])),
                      'biases':tf.Variable(tf.random_normal(n_nodes_hl1))}
    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                      'biases':tf.Variable(tf.random_normal(n_nodes_hl2))}
    hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                      'biases':tf.Variable(tf.random_normal(n_nodes_hl3))}
    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                      'biases':tf.Variable(tf.random_normal(n_classes))}

    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']) + hidden_1_layer['biases'])
    l1 = tf.nn.relu(l1)

    l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']) + hidden_2_layer['biases'])
    l2 = tf.nn.relu(l2)

    l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']) + hidden_3_layer['biases'])
    l3 = tf.nn.relu(l3)

    output = tf.matmul(l3, output_layer['weights']) + output_layer['biases']

    return output

def train_neural_network(x):
    prediction = neural_model(x)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    hm_epochs = 10

    with tf.Session as sess:
        sess.run(tf.global_variables_initializer())

        for epoch in hm_epochs:
            epoch_loss = 0
            for _ in range(int(mnist.train.num_examples/batch_size)):
                x, y = mnist.train.next_batch(batch_size)
                _, c = sess.run([optimizer, cost], feed_dict={x: x, y:y})
                epoch_loss += c
            print('Epoch: ', epoch, 'completed out of', hm_epochs, 'loss: ', epoch_loss)
        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y,1))

        ac = tf.reduce_mean(tf.cast(correct, 'float'))
        print('acc: ', ac.eval({x:mnist.test_images, y:mnist.test_labels}))

train_neural_network(x)

But it raises this error:

ValueError: shape must be rank 1 but is rank 0 for 'random_normal1/:...' with shapes[]

EDIT: Here is the full Traceback:

Traceback (most recent call last):
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/common_shapes.py", line 686, in _call_cpp_shape_fn_impl
    input_tensors_as_shapes, status)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape must be rank 1 but is rank 0 for 'random_normal_1/RandomStandardNormal' (op: 'RandomStandardNormal') with input shapes: [].

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "a.py", line 60, in <module>
    train_neural_network(x)
  File "a.py", line 39, in train_neural_network
    prediction = neural_model(x)
  File "a.py", line 17, in neural_model
    'biases':tf.Variable(tf.random_normal(n_nodes_hl1))}
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/random_ops.py", line 76, in random_normal
    shape_tensor, dtype, seed=seed1, seed2=seed2)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_random_ops.py", line 420, in _random_standard_normal
    seed2=seed2, name=name)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2958, in create_op
    set_shapes_for_outputs(ret)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2209, in set_shapes_for_outputs
    shapes = shape_func(op)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2159, in call_with_requiring
    return call_cpp_shape_fn(op, require_shape_fn=True)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/common_shapes.py", line 627, in call_cpp_shape_fn
    require_shape_fn)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/common_shapes.py", line 691, in _call_cpp_shape_fn_impl
    raise ValueError(err.message)
ValueError: Shape must be rank 1 but is rank 0 for 'random_normal_1/RandomStandardNormal' (op: 'RandomStandardNormal') with input shapes: [].

I'm just not sure what must i do exactly. There must no error I guess...

Thanks for help and sorry for bad english if I have any mistakes.

EDIT2: I added full of code now. I'm almost ensure that my code is same as video of sentdex. This code working on that guy... Where am i wrong?

Answer 1

I'll update this answer as needed once you provide the full code of neural_model since the error is in there, but already from the traceback I see you have in there:

'biases':tf.Variable(tf.random_normal(n_nodes_hl1))

tf.random_normalneeds a list as shape.

Change tf.random_normal(n_nodes_hl1) to tf.random_normal( [n_nodes_hl1] ) and it should work (or at least move on to the next error..)

Update: What written above applies also to all other calls of tf.random_normal

Update 2: About the add() problem, you have:

l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']) + hidden_1_layer['biases'])

That + is wrong. Either you use tf.add(tensor1, tensor2) or you do tensor1 + tensor2 (TF takes care of the rest). Your code is a mixture of the two that is equivalent to tf.add( tensor1 + tensor2 ), so it complains because add is missing the second parameter.