CODEWITHSUNDEEP
pythontensorflowreshape
Jespar
Jespar

Reputation: 1026

Tensorflow dimensionality issue with reshape

I have created this code but I am stuck with a dimensionality error

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.contrib.rnn.python.ops import rnn_cell, rnn
from time import time

# 2) Import MNIST data http://yann.lecun.com/exdb/mnist/
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x_train = mnist.train.images 

# Define the appropriate model and variables (USER INPUTS)
batch = 100 # Define the size of the batch
units = 32 # Number of units of each network
recurrent_layers = 1 # Number of layers
nnclasses = 10 # MNIST classes (0-9)
steps = x_train.shape[1] # 784
feed = 1 # Number of pixels to be fed into the model
recurrent_layers = 1 # Define the size of the recurrent layers
dropout = 1 # 

x = tf.placeholder(tf.float32,[None, None]) # batch(100)x784
x_resh = tf.reshape(x,[-1,steps,1]) # (100, 784, 1)
keep_prob = tf.placeholder(tf.float32,shape=[]) 

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

w_fc = weight_variable([units, nnclasses])

cell = tf.contrib.rnn.GRUCell(units)
cell = tf.contrib.rnn.DropoutWrapper(cell, input_keep_prob = keep_prob)
cell = tf.contrib.rnn.MultiRNNCell([cell] * recurrent_layers)
cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob = keep_prob)
outputs, final_state = tf.nn.dynamic_rnn(cell, x_resh, dtype=tf.float32)
output = outputs[:,:-1, :]
logits = tf.matmul(tf.reshape(output,[-1,tf.shape(w_fc)[0]]), w_fc) # [78300, 10]
y = tf.reshape(x[:,1:], [-1, nnclasses]) # [7830, 10]
K = [tf.shape(y)[0], tf.shape(logits)[0]]

sess = tf.InteractiveSession()   
sess.run(tf.global_variables_initializer())

def binarize(images, threshold=0.1):
    return (threshold < images).astype('float32')
batch_x, _ = mnist.train.next_batch(batch)
batch_x = binarize(batch_x, threshold=0.1)

return = sess.run(K, feed_dict={x: batch_x, keep_prob: 1.0})

Which returns [7830, 78300]. The issue is these two numbers should have been the same. They are the rows of y and logits, and if they are not similar I cannot compare them in a cross entropy setting. Can someone please let me know where the process is wrong? Actually, the (y) should return [78300, 10] but I do not know why.

Upvotes: 1

Views: 142

Answers (1)

Mark McDonald
Mark McDonald

Reputation: 8210

y = tf.reshape(x[:,1:], [-1, nnclasses]) # [7830, 10]

Your x tensor is of shape batch(100)x784, so x[:1,:] is 100x783. This is a total of 78,300 elements. 78300x10 would be 783,000, you simply don't have enough data in x to make it that shape.

Do you mean to use logits as a parameter of y? Assuming y is your output, using x as a param means you've bypassed the entire network.

Upvotes: 1

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