Value Error in tensorflow - python-3.x

Question

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/temp/data", one_hot=True)
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 10
batch_size = 100
# matrix = height * width
x = tf.placeholder('float', [None, 784])
y = tf.placeholder('float')
# defining the neural network
def neural_network_model(data):
hiddenLayer1 = {'weights': tf.Variable(tf.random_normal([784, n_nodes_hl1])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}
hiddenLayer2 = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}
hiddenLayer3 = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl3]))}
outputLayer = {'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, hiddenLayer1['weights']), hiddenLayer1['biases'])
l1 = tf.nn.relu(l1)
l2 = tf.add(tf.matmul(l1, hiddenLayer2['weights']), hiddenLayer2['biases'])
l2 = tf.nn.relu(l2)
l3 = tf.add(tf.matmul(l2, hiddenLayer3['weights']), hiddenLayer3['biases'])
l3 = tf.nn.relu(l3)
output = tf.matmul(l3, outputLayer['weights']), outputLayer['biases']
return output
# training the network
def train_neural_network(x):
prediction = neural_network_model(x)
cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(prediction,tf.squeeze(y)))
#cost = tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y)
#cost = tf.reduce_mean(cost) * 100
optimizer = tf.train.AdamOptimizer(0.003).minimize(cost)
# cycles feed forward + backprop
numberOfEpochs = 10
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
#dealing with training data
for epoch in range(numberOfEpochs):
epoch_loss = 0
for _ in range(int(mnist.train.num_examples / batch_size)):
epoch_x, epoch_y = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
epoch_loss += c
print('Epoch', epoch, ' completed out of ', numberOfEpochs, ' loss: ', epoch_loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
print('Accuracy: ', accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
train_neural_network(x)
I am new to Tensorflow and I am trying to train my model to read datasets. But every time I run the code, I get this error:
Traceback (most recent call last):
File "firstAI.py", line 87, in
train_neural_network(x)
File "firstAI.py", line 62, in train_neural_network
cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(prediction,tf.squeeze(y)));
File "/home/phillipus/.local/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 1935, in sparse_softmax_cross_entropy_with_logits
labels, logits)
File "/home/phillipus/.local/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 1713, in _ensure_xent_args
"named arguments (labels=..., logits=..., ...)" % name)
ValueError: Only call sparse_softmax_cross_entropy_with_logits with named arguments (labels=..., logits=..., ...)
Looks like the problem is at the "cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(prediction,tf.squeeze(y)))" and the calling of the "train_neural_network(x)" function. I am new to Tensorflow so my troubleshooting isn't at its best, anyone to help me?

Answer 1

Maybe you could try using tf.nn.softmax_cross_entropy_with_logits rather than tf.nn.sparse_softmax_cross_entropy_with_logits inside the cost calculation.
However, if you want to continue to use tf.nn.sparse_softmax_cross_entropy_with_logits then this link might help: Tensorflow ValueError: Only call `sparse_softmax_cross_entropy_with_logits` with named arguments .
By the way, what are the versions of tensorflow and python you're using?
Try running this:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/temp/data", one_hot=True)
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 10
batch_size = 100
# matrix = height * width
x = tf.placeholder('float', [None, 784])
y = tf.placeholder('float')
# defining the neural network
def neural_network_model(data):
hiddenLayer1 = {'weights': tf.Variable(tf.random_normal([784,
n_nodes_hl1])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}
hiddenLayer2 = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}
hiddenLayer3 = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl3]))}
outputLayer = {'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, hiddenLayer1['weights']), hiddenLayer1['biases'])
l1 = tf.nn.relu(l1)
l2 = tf.add(tf.matmul(l1, hiddenLayer2['weights']), hiddenLayer2['biases'])
l2 = tf.nn.relu(l2)
l3 = tf.add(tf.matmul(l2, hiddenLayer3['weights']), hiddenLayer3['biases'])
l3 = tf.nn.relu(l3)
output = tf.add(tf.matmul(l3, outputLayer['weights']),outputLayer['biases'])
return output
prediction = neural_network_model(x)
cost = tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y)
optimizer = tf.train.AdamOptimizer(0.003).minimize(cost)
# cycles feed forward + backprop
numberOfEpochs = 10
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
#dealing with training data
for epoch in range(numberOfEpochs):
epoch_loss = 0
for _ in range(int(mnist.train.num_examples / batch_size)):
epoch_x, epoch_y = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
epoch_loss += c
print('Epoch', epoch, ' completed out of ', numberOfEpochs, ' loss: ', epoch_loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
print('Accuracy: ', accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

Answer 2

Try this code
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/temp/data/", one_hot = True)
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 10
batch_size = 100
#height x width
x = tf.placeholder('float',[None, 784])
y = tf.placeholder('float')
def neural_network_model(data):
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_network_model(x)
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction,labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
hm_epochs = 10
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for epoch in range(hm_epochs):
epoch_loss = 0
for _ in range(int(mnist.train.num_examples/batch_size)):
epoch_x,epoch_y = mnist.train.next_batch(batch_size)
_,epoch_c = sess.run([optimizer, cost], feed_dict = {x: epoch_x, y: epoch_y})
epoch_loss += epoch_c
print('Epoch', epoch, 'completed out of ', hm_epochs, 'loss: ', epoch_loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:', accuracy.eval({x:mnist.test.images, y: mnist.test.labels}))
train_neural_network(x)