TF error: Shapes of both tensors to match - tensorflow

Question

I try to implement a CNN model based on MNIST tutorial on TF website.
Here is my code
import tensorflow as tf
import numpy as np
from tensorflow.contrib import learn
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
# Input Layer
# Reshape X to 4-D tensor: [batch_size, width, height, channels]
# breaKHis images are 32x32 pixels, and have three color channel
input_layer = tf.reshape(features, [-1, 32, 32, 3])
# Convolutional Layer #1
# Computes 32 features using a 5x5 filter with ReLU activation.
# Padding is added to preserve width and height.
# Input Tensor Shape: [batch_size, 32, 32, 1]
# Output Tensor Shape: [batch_size, 32, 32, 32]
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
#print conv1.get_shape().as_list()
# Pooling Layer #1
# First max pooling layer with a 2x2 filter and stride of 2
# Input Tensor Shape: [batch_size, 32, 32, 32]
# Output Tensor Shape: [batch_size, 16, 16, 32]
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
#print pool1.get_shape().as_list()
# Convolutional Layer #2
# Computes 32 features using a 5x5 filter.
# Padding is added to preserve width and height.
# Input Tensor Shape: [batch_size, 16, 16, 32]
# Output Tensor Shape: [batch_size, 16, 16, 32]
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
#print conv2.get_shape().as_list()
# Pooling Layer #2
# Second max pooling layer with a 3x3 filter and stride of 2
# Input Tensor Shape: [batch_size, 16, 16, 32]
# Output Tensor Shape: [batch_size, 8, 8, 32]
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
#print pool2.get_shape().as_list()
# Convolutional Layer #3
# Computes 64 features using a 5x5 filter.
# Padding is added to preserve width and height.
# Input Tensor Shape: [batch_size, 8, 8, 32]
# Output Tensor Shape: [batch_size, 8, 8, 64]
conv3 = tf.layers.conv2d(
inputs=pool2,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
#print conv3.get_shape().as_list()
# Pooling Layer #3
# Second max pooling layer with a 3x3 filter and stride of 2
# Input Tensor Shape: [batch_size, 8, 8, 64]
# Output Tensor Shape: [batch_size, 4, 4, 64]
pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2)
# Flatten tensor into a batch of vectors
# Input Tensor Shape: [batch_size, 4, 4, 64]
# Output Tensor Shape: [batch_size, 4 * 4 * 64]
pool3Shape = pool3.get_shape().as_list()
#print pool3Shape
pool2_flat = tf.reshape(pool2, [-1, 4*4*64])
# Dense Layer
# Densely connected layer with 64 neurons
# Input Tensor Shape: [batch_size, 4 * 4 * 64]
# Output Tensor Shape: [batch_size, 64]
dense = tf.layers.dense(inputs=pool2_flat, units=64, activation=tf.nn.relu)
# Add dropout operation; 0.6 probability that element will be kept
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
# Logits layer
# Input Tensor Shape: [batch_size, 64]
# Output Tensor Shape: [batch_size, 2]
logits = tf.layers.dense(inputs=dropout, units=2)
loss = None
train_op = None
# Calculate Loss (for both TRAIN and EVAL modes)
if mode != learn.ModeKeys.INFER:
onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=2)
loss = tf.losses.softmax_cross_entropy(
onehot_labels=onehot_labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == learn.ModeKeys.TRAIN:
train_op = tf.contrib.layers.optimize_loss(
loss=loss,
global_step=tf.contrib.framework.get_global_step(),
learning_rate=0.001,
optimizer="SGD")
# Generate Predictions
predictions = {
"classes": tf.argmax(
input=logits, axis=1),
"probabilities": tf.nn.softmax(
logits, name="softmax_tensor")
}
# Return a ModelFnOps object
return model_fn_lib.ModelFnOps(
mode=mode, predictions=predictions, loss=loss, train_op=train_op)
And it throws me the error: InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [1024,64] rhs shape= [2048,64]
I think there should be something wrong in the last FC layer but don't know where it is.

Answer 1

You can check this answer,
Tensorflow Assign requires shapes of both tensors to match. lhs shape= [20] rhs shape= [48]
Maybe you can install the previous version and try it again.