"TypeError："float"对象在编写简单 GAN 时无法解释为 Python 3.7.1 中的整数"错误

Question

我是Python和编程的全新。我正在尝试编码一个简单的GAN来使用Keras数据集（请参阅下面的超链接到教程）。我收到两个警告，然后是一个错误：

TypeError: 'float' object cannot be interpreted as an integer.

任何帮助将不胜感激。

详细信息：

Python 3.7.1，Mac OS High Sierra 10.13.6。我正在为Python代码使用空闲，并通过终端运行程序。

错误

警告：TensorFlow：来自/users/darren/miniconda3/lib/python3.7/site-packages/tensorflow/tensorflow/python/framework/op_def_library.py.py:263：colocate_with：colocate_with（来自tensorflow.python.framework.ops）是并将在以后的版本中删除。

更新的说明：由placer自动处理的共系。

警告：TensorFlow：来自/users/darren/miniconda3/lib/python3.7/site-packages/keras/backend/backend/backend/tensorflow _backend.3445：with tensorflow.python.nn_ops.nn_ops.nn_ops with keep_prob是弃用并将在以后的版本中删除。

更新的说明：请使用rate代替keep_prob。费率应设置为rate = 1 - keep_prob。

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追溯（最近的最新电话）：

文件" gan.py"，第91行，in 火车（400，128）文件" gan.py"，第75行，火车 对于_在tqdm（range（batch_count））中： TypeError：" float"对象不能解释为整数

代码：

import os
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from keras.layers import Input
from keras.models import Model, Sequential
from keras.layers.core import Dense, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.datasets import mnist
from keras.optimizers import Adam
from keras import initializers
os.environ["KERAS_BACKEND"] = "tensorflow"
np.random.seed(10)
random_dim = 100
def load_minst_data():
        (x_train, y_train), (x_test, y_test) = mnist.load_data()
        x_train = (x_train.astype(np.float32) - 127.5)/127.5
        x_train = x_train.reshape(60000, 784)
        return (x_train, y_train, x_test, y_test)
def get_optimizer():
        return Adam(lr=0.0002, beta_1=0.5)
def get_generator(optimizer):
        generator = Sequential()
        generator.add(Dense(256, input_dim=random_dim, kernel_initializer=initializers.RandomNormal(stddev=0.02)))
        generator.add(LeakyReLU(0.2))
        generator.add(Dense(512))
        generator.add(LeakyReLU(0.2))
        generator.add(Dense(1024))
        generator.add(LeakyReLU(0.2))
        generator.add(Dense(784, activation='tanh'))
        generator.compile(loss='binary_crossentropy', optimizer=optimizer)
        return generator
def get_discriminator(optimizer):
        discriminator = Sequential()
        discriminator.add(Dense(1024, input_dim=784, kernel_initializer=initializers.RandomNormal(stddev=0.02)))
        discriminator.add(LeakyReLU(0.2))
        discriminator.add(Dropout(0.3))
        discriminator.add(Dense(512))
        discriminator.add(LeakyReLU(0.2))
        discriminator.add(Dropout(0.3))
        discriminator.add(Dense(256))
        discriminator.add(LeakyReLU(0.2))
        discriminator.add(Dropout(0.3))
        discriminator.add(Dense(1, activation='sigmoid'))
        discriminator.compile(loss='binary_crossentropy', optimizer=optimizer)
        return discriminator
def get_gan_network(discriminator, random_dim, generator, optimizer):
        discriminator.trainable = False
        gan_input = Input(shape=(random_dim,))
        x = generator(gan_input)
        gan_output = discriminator(x)
        gan = Model(inputs=gan_input, outputs=gan_output)
        gan.compile(loss='binary_crossentropy', optimizer=optimizer)
        return gan
def plot_generated_images(epoch, generator, examples=100, dim=(10, 10), figsize=(10, 10)):
        noise = np.random.normal(0, 1, size=[examples, random_dim])
        generated_images = generator.predict(noise)
        generated_images = generated_images.reshape(examples, 28, 28)
        plt.figure(figsize=figsize)
        for i in range(generated_images.shape[0]):
                plt.subplot(dim[0], dim[1], i+1) 
                plt.imshow(generated_images[i], interpolation='nearest', cmap='gray_r')
                plt.axis('off')
        plt.tight_layout()
        plt.savefig('gan_generated_image_epoch_%d.png' % epoch)
def train(epochs=1, batch_size=128):
        x_train, y_train, x_test, y_test = load_minst_data()
        batch_count = x_train.shape[0] / batch_size
        adam = get_optimizer()
        generator = get_generator(adam)
        discriminator = get_discriminator(adam)
        gan = get_gan_network(discriminator, random_dim, generator, adam)
        for e in range(1, epochs+1):
                print ('-'*15, 'Epoch %d' % e, '-'*15)
                for _ in tqdm(range(batch_count)):
                        noise = np.random.normal(0, 1, size=[batch_size, random_dim])
                        image_batch = x_train[np.random.randint(0, x_train.shape[0], size=batch_size)]
                        generated_images = generator.predict(noise)|
                        X = np.concatenate([image_batch, generated_images])
                        y_dis = np.zeros(2*batch_size)
                        y_dis[:batch_size] = 0.9
                        discriminator.trainable = True
                        discriminator.train_on_batch(X, y_dis)
                        noise = np.random.normal(0, 1, size=[batch_size, random_dim])
                        y_gen = np.ones(batch_size)
                        discriminator.trainable = False
                        gan.train_on_batch(noise, y_gen)
               if e == 1 or e % 20 == 0:
                        plot_generated_images(e, generator)
if __name__ == '__main__':
        train(400, 128)

链接到教程

Answer 1

问题来自行x_train.shape[0] / batch_size。默认情况下，在python3的较新版本中，/操作员执行浮点部门而不是div。

如果您希望结果是整数，则应使用//运算符，该操作员的行为与div：

一样

x_train.shape[0] // batch_size。