Tensorflow won't matmul inputs and weights. "Dimensions must be equal" - tensorflow

Question

I've been working on a simple tensor flow neural network. My input placeholder is
x = tf.placeholder(tf.float32, shape=[None, 52000, 3]).
My weight matrix is initialized to all zeros as
W = tf.Variable(tf.zeros([52000, 10])).
I tried different combinations with and without the 3 for color channels, but I guess I'm just not understanding the dimensionality because I got the error:
Traceback (most recent call last): File
"C:\Users\Everybody\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py",
line 686, in _call_cpp_shape_fn_impl
input_tensors_as_shapes, status) File "C:\Users\Everybody\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\errors_impl.py",
line 473, in exit
c_api.TF_GetCode(self.status.status)) tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape
must be rank 2 but is rank 3 for 'MatMul' (op: 'MatMul') with input
shapes: [?,52000,3], [52000,10].
During handling of the above exception, another exception occurred:
Traceback (most recent call last): File "rating.py", line 65, in
y = tf.matmul(x, W) + b # "fake" outputs to train/test File "C:\Users\Everybody\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\math_ops.py",
line 1891, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name) File
"C:\Users\Everybody\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\gen_math_ops.py",
line 2436, in _mat_mul
name=name) File "C:\Users\Everybody\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py",
line 787, in _apply_op_helper
op_def=op_def) File "C:\Users\Everybody\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py",
line 2958, in create_op
set_shapes_for_outputs(ret) File "C:\Users\Everybody\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py",
line 2209, in set_shapes_for_outputs
shapes = shape_func(op) File "C:\Users\Everybody\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py",
line 2159, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True) File "C:\Users\Everybody\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py",
line 627, in call_cpp_shape_fn
require_shape_fn) File "C:\Users\Everybody\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py",
line 691, in _call_cpp_shape_fn_impl
raise ValueError(err.message) ValueError: Shape must be rank 2 but is rank 3 for 'MatMul' (op: 'MatMul') with input shapes: [?,52000,3],
[52000,10].
At first, I thought my next_batch() function was the culprit because I had to make my own due to the fact that I uploaded my images "manually" using scipy.misc.imread(), whose definition reads:
q = 0
def next_batch(batch_size):
x = images[q:q + batch_size]
y = one_hots[q:q + batch_size]
q = (q + batch_size) % len(images)
return x, y
However, after looking through, I don't see what's wrong with this, so I imagine that I'm just confused about dimensionality. It is supposed to be a "flattened" 200x260 color image. It just occurred to me now that maybe I have to flatten the color channels as well? I will place my full code below if curious. I'm a bit new to Tensorflow, so thanks, all. (Yes, it is not a CNN yet, I decided to start simple just to make sure I'm importing my dataset right. And, I know it is tiny, I'm starting my dataset small too.)
############# IMPORT DEPENDENCIES ####################################
import tensorflow as tf
sess = tf.InteractiveSession() #start session
import scipy.misc
import numpy as np
######################################################################
#SET UP DATA #########################################################
images = []
one_hots = []
########### IMAGES ##################################################
#put all the images in a list
for i in range(60):
images.append(scipy.misc.imread('./shoes/%s.jpg' % str(i+1)))
print("One image appended...\n")
#normalize them, "divide" by 255
for image in images:
print("One image normalized...\n")
for i in range(260):
for j in range(200):
for c in range(3):
image[i][j][c]/=255
for image in images:
tf.reshape(image, [52000, 3])
########################################################################
################# ONE-HOT VECTORS ######################################
f = open('rateVectors.txt')
lines = f.readlines()
for i in range(0, 600, 10):
fillerlist = []
for j in range(10):
fillerlist.append(float(lines[i+j][:-1]))
one_hots.append(fillerlist)
print("One one-hot vector added...\n")
########################################################################3
#set placeholders and such for input, output, weights, biases
x = tf.placeholder(tf.float32, shape=[None, 52000, 3])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
W = tf.Variable(tf.zeros([52000, 10])) # These are our weights and biases
b = tf.Variable(tf.zeros([10])) # initialized as zeroes.
#########################################################################
sess.run(tf.global_variables_initializer()) #initialize variables in the session
y = tf.matmul(x, W) + b # "fake" outputs to train/test
##################### DEFINING OUR MODEL ####################################
#our loss function
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
#defining our training as gradient descent
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
###################### TRAINING #############################################
#################### OUR CUSTOM BATCH FUNCTION ##############################
q = 0
def next_batch(batch_size):
x = images[q:q + batch_size]
y = one_hots[q:q + batch_size]
q = (q + batch_size) % len(images)
return x, y
#train
for i in range(6):
batch = next_batch(10)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
print("Batch Number: " + i + "\n")
print("Done training...\n")
################ RESULTS #################################################
#calculating accuracy
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#print accuracy
print(accuracy.eval(feed_dict={x: images, y_: one_hots}))

Answer 1

Your placeholder should have the dimension [None, 200, 260, 3] where None is the batch size, 200, 260 is the image size, and 3 is the channels.
Your weights should be [filter_height, filter_width, num_channels, num_filters]
Your bias should be [num_filters]
And the dimensions for the labels should be [None, num_classes] where None is the batch size, and num_classes is the number of classes that your images have.
These are just to make sure that math works.
I took these codes from here