Python-TypeError:';浮动';对象不能解释为整数
我有以下代码:Python-TypeError:';浮动';对象不能解释为整数,python,tensorflow,Python,Tensorflow,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.float3
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')
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
批量225.0
第0纪元
回溯(最近一次呼叫最后一次):
文件“cnn.py”,第66行,在
对于范围内的i(0,长度(数据),批次大小):
TypeError:“float”对象不能解释为整数
如何解决此问题
谢谢。正如伯尼所说,您可以使用楼层分割。这是正确的,但基于您在for循环中尝试执行的操作,我回答了您的问题,并展示了如何在代码中使用它。使用
int(batch\u size)
将batch\u size
转换为整数是在for循环中使用它的正确方法
for i in range(0, len(data), int(batch_size)):
# process data
不知道为什么投票被否决。您可以改为使用:
batch_size = len(data) // 200