猫狗图像识别 求助！！！

猫狗图像识别遇到问题，求助

错误如下：
TypeError: Value passed to parameter ‘shape’ has DataType float32 not in list of allowed values: int32, int64

代码如下：

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# author: JX
# data: 20181101

import dataset
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
import time
from datetime import timedelta
import math
import random
import numpy as np
#from tensorflow import set_random_seed
from numpy.random import seed
seed(10)
tf.random.set_seed(20)

batch_size = 32

classes = ['dogs','cats']
num_classes = len(classes)

validation_size = 0.2
img_size = 64
num_channels = 3
train_path = 'training_data'

data = dataset.read_train_sets(train_path,img_size,classes,validation_size=validation_size)

print("Complete reading input data.Will Now print a snippet of it")
print("Number of files in Training-set:\t\t{}".format(len(data.train.labels)))
print("Number of files in Validation-set:\t\t{}".format(len(data.valid.labels)))

config = tf.compat.v1.ConfigProto(allow_soft_placement=True)
session = tf.compat.v1.Session()
x = tf.compat.v1.placeholder(tf.float32,shape=[None,img_size,img_size,num_channels],name='x')

y_true = tf.compat.v1.placeholder(tf.float32,shape=[None,num_classes],name='y_true')
y_true_cls = tf.argmax(y_true)


filter_size_conv1 = 3
num_filters_conv1 = 32

filter_size_conv2 = 3
num_filters_conv2 = 32

filter_size_conv3 = 3
num_filters_conv3 = 64

fc_layer_size = 1024

def create_weights(shape):
    return tf.compat.v1.random.truncated_normal(tf.compat.v1.random.truncated_normal(shape,stddev=0.05))

def create_biases(size):
    return tf.Variable(tf.constant(0.05,shape=[size]))

def create_convolutional_layer(input,
                               num_input_channels,
                               conv_filter_size,
                               num_filters):
    weights = create_weights(shape=[conv_filter_size,conv_filter_size,num_input_channels,num_filters])
    biases = create_biases(num_filters)

    layer = tf.nn.conv2d(input=input,
                         filter=weights,strides=[1,1,1,1],
                         padding='SAME')
    layer += biases
    layer = tf.nn.relu(layer)

    layer = tf.nn.max_pool(value=layer,
                           ksize=[1,2,2,1],
                           strides=[1,2,2,1],
                           padding='SAME')
    return layer

def create_flatten_layer(layer):
    layer_shape = layer.get_shape()
    num_features = layer_shape[1:4].num_elements()
    layer = tf.reshape(layer,[-1,num_features])

    return layer

def create_fc_layer(input,
                    num_inputs,
                    num_outputs,
                    use_relu=True):
    weights = create_weights(shape=[num_inputs,num_outputs])
    biases = create_biases(num_outputs)
    layer = tf.matmul(input,weights) + biases
    layer = tf.nn.dropout(layer,keep_prob=0.7)
    if use_relu:
        layer = tf.nn.relu(layer)

    return layer

layer_conv1 = create_convolutional_layer(input=x,
                                         num_input_channels=num_channels,
                                         conv_filter_size=filter_size_conv1,
                                         num_filters=num_filters_conv1)
layer_conv2 = create_convolutional_layer(input=layer_conv1,
                                         num_input_channels=num_filters_conv1,
                                         conv_filter_size=filter_size_conv2,
                                         num_filters=num_filters_conv2)
layer_conv3 = create_convolutional_layer(input=layer_conv2,
                                         num_input_channels=num_filters_conv2,
                                         conv_filter_size=filter_size_conv3,
                                         num_filters=num_filters_conv3)

layer_flat = create_flatten_layer(layer_conv3)

layer_fc1 = create_fc_layer(input=layer_flat,
                            num_inputs=layer_flat.get_shape()[1:4].num_elements(),
                            num_outputs=fc_layer_size,
                            use_relu=True)

layer_fc2 = create_fc_layer(input=layer_fc1,
                            num_inputs=fc_layer_size,
                            num_outputs=num_classes,
                            use_relu=False)

y_pred = tf.nn.softmax(layer_fc2,name='y_pred')

y_pred_cls = tf.argmax(y_pred,dimension=1)
session.run(tf.global_variables_initializer())
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,labels=y_true)
cost = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)
correct_prediction = tf.equal(y_pred_cls,y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

session.run(tf.global_variables_initializer())

def show_pregress(epoch,feed_dict_train,feed_dict_validata,val_loss,i):
    acc = session.run(accuracy,feed_dict=feed_dict_train)
    val_acc = session.run(accuracy,feed_dict=feed_dict_validata)
    msg = "Training Epoch {0}---iterations: {1}---Training Accuracy:{2:>6.1%}," \
          "Validation Accuracy:{3:>6.1%},Validation Loss:{4:.3f}"
    print(msg.format(epoch+1,i,acc,val_acc,val_loss))

total_iterations = 0

saver = tf.train.Saver()
def train(num_iteration):
    global  total_iterations

    for i in range(total_iterations,total_iterations + num_iteration):
        x_batch,y_true_batch,_,cls_batch = data.train.next_batch(batch_size)
        x_valid_batch,y_valid_batch,_,valid_cls_batch = data.valid.next_batch(batch_size)

        feed_dict_tr = {x: x_batch,y_true:y_true_batch}
        feed_dict_val = {x: x_valid_batch, y_true: y_valid_batch}

        session.run(optimizer,feed_dict=feed_dict_tr)

        if i % int(data.train.num_examples/batch_size) == 0:
            val_loss = session.run(cost,feed_dict=feed_dict_val)
            epoch = int(i / int(data.train.num_examples/batch_size))

            show_pregress(epoch,feed_dict_tr,feed_dict_val,val_loss,i)
            saver.save(session,'./dogs-cats-model/dog-cat.ckpt',global_step=i)

    total_iterations += num_iteration

train(num_iteration=10000)