tensorflow ValueError: Dimension 0 in both shapes must be equal

Question

I am currently studying TensorFlow. I am trying to create a NN which can accurately assess a prediction model and assign it a score. My plan right now is to combine scores from already existing programs run them through a mlp while comparing them to true values. I have played around with the MNIST data and I am trying to apply what I have learnt to my project. Unfortunately i have a problem

def multilayer_perceptron(x, w1):
   # Hidden layer with RELU activation
   layer_1 = tf.matmul(x, w1)
   layer_1 = tf.nn.relu(layer_1)
   # Output layer with linear activation
   #out_layer = tf.matmul(layer_1, w2)
   return layer_1

def my_mlp (trainer, trainer_awn, learning_rate, training_epochs, n_hidden, n_input, n_output):
trX, trY= trainer, trainer_awn
#create placeholders
x = tf.placeholder(tf.float32, shape=[9517, 5])
y_ = tf.placeholder(tf.float32, shape=[9517, ])
#create initial weights
w1 = tf.Variable(tf.zeros([5, 1]))
#predicted class and loss function
y = multilayer_perceptron(x, w1)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
#training
train_step = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
with tf.Session() as sess:
    # you need to initialize all variables
    sess.run(tf.initialize_all_variables())
    print("1")
    for i in range(training_epochs + 1):
        sess.run([train_step], feed_dict={x: [trX['V7'], trX['V8'], trX['V9'], trX['V10'], trX['V12']], y_: trY})
return 

The code gives me this error

ValueError: Dimension 0 in both shapes must be equal, but are 9517 and 1

This error occurs when running the line for cross_entropy. I don't understand why this is happing, if you need any more information I would be happy to give it to you.

Answer 1

in your case, y has shape [9517, 1] while y_ has shape [9517]. they are not campatible. Please try to reshape y_ using tf.reshape(y_, [-1, 1])

Answer 2

This was caused by the weights.hdf5 file being incompatible with the new data in the repository. I have updated the repo and it should work now.