Question

我有以下代码：

import numpy as np
import matplotlib.pyplot as plt
import cifar_tools
import tensorflow as tf

data, labels = cifar_tools.read_data('C:\\Users\\abc\\Desktop\\temp')

x = tf.placeholder(tf.float32, [None, 24 * 24])
y = tf.placeholder(tf.float32, [None, 2])

w1 = tf.Variable(tf.random_normal([5, 5, 1, 64]))
b1 = tf.Variable(tf.random_normal([64]))

w2 = tf.Variable(tf.random_normal([5, 5, 64, 64]))
b2 = tf.Variable(tf.random_normal([64]))

w3 = tf.Variable(tf.random_normal([6*6*64, 1024]))
b3 = tf.Variable(tf.random_normal([1024]))

w_out = tf.Variable(tf.random_normal([1024, 2]))
b_out = tf.Variable(tf.random_normal([2]))

def conv_layer(x,w,b):
    conv = tf.nn.conv2d(x,w,strides=[1,1,1,1], padding = 'SAME')
    conv_with_b = tf.nn.bias_add(conv,b)
    conv_out = tf.nn.relu(conv_with_b)
    return conv_out

def maxpool_layer(conv,k=2):
    return tf.nn.max_pool(conv, ksize=[1,k,k,1], strides=[1,k,k,1], padding='SAME')

def model():
    x_reshaped = tf.reshape(x, shape=[-1,24,24,1])

    conv_out1 = conv_layer(x_reshaped, w1, b1)
    maxpool_out1 = maxpool_layer(conv_out1)
    norm1 = tf.nn.lrn(maxpool_out1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)

    conv_out2 = conv_layer(norm1, w2, b2)
    maxpool_out2 = maxpool_layer(conv_out2)
    norm2 = tf.nn.lrn(maxpool_out2, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)

    maxpool_reshaped = tf.reshape(maxpool_out2, [-1,w3.get_shape().as_list()[0]])
    local = tf.add(tf.matmul(maxpool_reshaped, w3), b3)
    local_out = tf.nn.relu(local)

    out = tf.add(tf.matmul(local_out, w_out), b_out)
    return out

model_op = model()

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(model_op, y))
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)

correct_pred = tf.equal(tf.argmax(model_op, 1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    onehot_labels = tf.one_hot(labels, 2, on_value=1.,off_value=0.,axis=-1)
    onehot_vals = sess.run(onehot_labels)
    batch_size = len(data) / 200
    print('batch size', batch_size)
    for j in range(0, 1000):
        print('EPOCH', j)
        for i in range(0, len(data), batch_size):
            batch_data = data[i:i+batch_size, :]
            batch_onehot_vals = onehot_vals[i:i+batch_size, :]
            _, accuracy_val = sess.run([train_op, accuracy], feed_dict={x: batch_data, y: batch_onehot_vals})
            if i % 1000 == 0:
                print(i, accuracy_val)
        print('DONE WITH EPOCH')

当我运行代码时，出现以下错误：

batch size 225.0
EPOCH 0
Traceback (most recent call last):
  File "cnn.py", line 66, in <module>
    for i in range(0, len(data), batch_size):
TypeError: 'float' object cannot be interpreted as an integer

如何解决此问题？

感谢。

Answer 1

您可以改为使用floor division：

batch_size = len(data) // 200

Answer 2

正如bernie所说，你可以使用地板分割。这是正确的，但根据您在for循环中尝试做的事情，我既回答了您的问题又展示了如何在您的代码中使用它。使用batch_size将int(batch_size)转换为整数是在for-loop中使用它的正确方法。

for i in range(0, len(data), int(batch_size)):
     # process data

不知道为什么这会被投票。

Python - TypeError：＆＃39; float＆＃39; object不能解释为整数

2 个答案: