ValueError: Cannot feed value of shape (3375, 50, 50, 2) for Tensor 'Reshape:0', which has shape '(?, 5000)' - tensorflow

Question

I am learning Tensorflow. Following is my code for MLP with TensorFlow. I have some issues with mismatching of data dimentions.
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
wholedataset = np.load('C:/Users/pourya/Downloads/WholeTrueData.npz')
data = wholedataset['wholedata'].astype('float32')
label = wholedataset['wholelabel'].astype('float32')
height = wholedataset['wholeheight'].astype('float32')
print(type(data[20,1,1,0]))
learning_rate = 0.001
training_iters = 5
display_step = 20
n_input = 3375
X = tf.placeholder("float32")
Y = tf.placeholder("float32")
weights = {
'wc1': tf.Variable(tf.random_normal([3, 3, 2, 1])),
'wd1': tf.Variable(tf.random_normal([3, 3, 1, 1]))
}
biases = {
'bc1': tf.Variable(tf.random_normal([1])),
'out': tf.Variable(tf.random_normal([1,50,50,1]))
}
mnist= data
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 2
batch_size = 100
x = tf.placeholder('float', shape = [None,50,50,2])
shape = x.get_shape().as_list()
dim = np.prod(shape[1:])
x_reshaped = tf.reshape(x, [-1, dim])
y = tf.placeholder('float', shape= [None,50,50,2])
shape = y.get_shape().as_list()
dim = np.prod(shape[1:])
y_reshaped = tf.reshape(y, [-1, dim])
def neural_network_model(data):
hidden_1_layer = {'weights':tf.Variable(tf.random_normal([5000,
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(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 range(hm_epochs):
epoch_loss = 0
for _ in range(int(n_input/batch_size)):
epoch_x = wholedataset['wholedata'].astype('float32')
epoch_y = wholedataset['wholedata'].astype('float32')
_, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y:
epoch_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))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:',accuracy.eval({x:mnist.test.images,
y:mnist.test.labels}))
train_neural_network(x)
I got the following error:
ValueError: Cannot feed value of shape (3375, 50, 50, 2) for Tensor 'Reshape:0', which has shape '(?, 5000)'
Does anyone know what is the issue with my code, and how can I fix it?
The data value is (3375, 50, 50, 2)
Thank you for anyone's input!

Answer 1

I think that the problem is that you use the same variable name x for the placeholder and the reshape, in lines
x = tf.placeholder('float', shape = [None,50,50,2])
and
x = tf.reshape(x, [-1, dim])
so that when you
feed_dict={x: your_val}
you are feeding the output of the reshape operation.
You should have different names, for instance
x_placeholder = tf.placeholder('float', shape = [None,50,50,2])
x_reshaped = tf.reshape(x, [-1, dim])
and then
feed_dict={x_placeholder: your_val}