Skip to content

Instantly share code, notes, and snippets.

@ackdav
Created November 30, 2016 09:53
Show Gist options
  • Save ackdav/30ba675a9407a79ae618be61a7b94397 to your computer and use it in GitHub Desktop.
Save ackdav/30ba675a9407a79ae618be61a7b94397 to your computer and use it in GitHub Desktop.
Source Code to "Sentdex Deep Learning with Neural Networks and Tensorflow" part 4
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 = 10
batch_size = 100
x = tf.placeholder('float',[None,784])
y = tf.placeholder('float')
def neural_net_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_net_model(x)
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,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)
@alexsandromf
Copy link

hello...please, can you help me?
An error occurred for me.

C:\Users\Alex\AppData\Local\Programs\Python\Python35\python.exe C:/Users/Alex/PycharmProjects/image_recognition/Neural_net_images_recog.py
Extracting /tmp/data/train-images-idx3-ubyte.gz
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "C:/Users/Alex/PycharmProjects/image_recognition/Neural_net_images_recog.py", line 67, in
train_neural_network(x)
File "C:/Users/Alex/PycharmProjects/image_recognition/Neural_net_images_recog.py", line 46, in train_neural_network
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(prediction, y))
File "C:\Users\Alex\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1558, in softmax_cross_entropy_with_logits
labels, logits)
File "C:\Users\Alex\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1512, in _ensure_xent_args
"named arguments (labels=..., logits=..., ...)" % name)
ValueError: Only call softmax_cross_entropy_with_logits with named arguments (labels=..., logits=..., ...)

do you know how can I fix it?
thank you

@ikbalsingh
Copy link

softmax_cross_entropy_with_logits( _sentinel=None, labels=None, logits=None, dim=-1, name=None )
Try passing the labels and logits parameters
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels = y, logits = prediction))

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment