Python 3.x ValueError：看起来您使用的是Keras 2，并且将'kernel_size'和'strips'作为整数位置参数传递

我是第四学期计算机科学专业的本科生，我正在学习机器学习

from __future__ import print_function
from keras import backend as K
K.common.set_image_dim_ordering('th') # ensure our dimension notation matches

from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Reshape
from keras.layers.core import Activation
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import UpSampling2D
from keras.layers.convolutional import Convolution2D, AveragePooling2D
from keras.layers.core import Flatten
from keras.optimizers import SGD, Adam
from keras.datasets import mnist
from keras import utils
import numpy as np
from PIL import Image, ImageOps
import argparse
import math

import os
import os.path

import glob
def generator_model():
    model = Sequential()
    model.add(Dense(input_dim=100, output_dim=1024))
    model.add(Activation('tanh'))
    model.add(Dense(128*8*8))
    model.add(BatchNormalization())
    model.add(Activation('tanh'))
    model.add(Reshape((128, 8, 8), input_shape=(128*8*8,)))
    model.add(UpSampling2D(size=(4, 4)))
    model.add(Convolution2D(64, 5, 5, border_mode='same'))
    model.add(Activation('tanh'))
    model.add(UpSampling2D(size=(4, 4)))
    model.add(Convolution2D(1, 5, 5, border_mode='same'))
    model.add(Activation('tanh'))
    return model


def discriminator_model():
    model = Sequential()
    model.add(Convolution2D(64, 5, 5, border_mode='same', input_shape=(1, 128, 128)))
    model.add(Activation('tanh'))
    model.add(AveragePooling2D(pool_size=(4, 4)))
    model.add(Convolution2D(128, 5, 5))
    model.add(Activation('tanh'))
    model.add(AveragePooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(256))
    model.add(Activation('tanh'))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))
    return model


def generator_containing_discriminator(generator, discriminator):
    model = Sequential()
    model.add(generator)
    discriminator.trainable = False
    model.add(discriminator)
    return model


def combine_images(generated_images):
    num = generated_images.shape[0]
    width = int(math.sqrt(num))
    height = int(math.ceil(float(num)/width))
    shape = generated_images.shape[2:]
    image = np.zeros((height*shape[0], width*shape[1]),
                     dtype=generated_images.dtype)
    for index, img in enumerate(generated_images):
        i = int(index/width)
        j = index % width
        image[i*shape[0]:(i+1)*shape[0], j*shape[1]:(j+1)*shape[1]] = \
            img[0, :, :]
    return image

model = generator_model()
print(model.summary())

def load_data(pixels=128, verbose=False):
    print("Loading data")
    X_train = []
    paths = glob.glob(os.path.normpath(os.getcwd() + '/logos/*.jpg'))
    for path in paths:
        if verbose: print(path)
        im = Image.open(path)
        im = ImageOps.fit(im, (pixels, pixels), Image.ANTIALIAS)
        im = ImageOps.grayscale(im)
        #im.show()
        im = np.asarray(im)
        X_train.append(im)
    print("Finished loading data")
    return np.array(X_train)

def train(epochs, BATCH_SIZE, weights=False):
    """
    :param epochs: Train for this many epochs
    :param BATCH_SIZE: Size of minibatch
    :param weights: If True, load weights from file, otherwise train the model from scratch. 
    Use this if you have already saved state of the network and want to train it further.
    """
    X_train = load_data()
    X_train = (X_train.astype(np.float32) - 127.5)/127.5
    X_train = X_train.reshape((X_train.shape[0], 1) + X_train.shape[1:])
    discriminator = discriminator_model()
    generator = generator_model()
    if weights:
        generator.load_weights('goodgenerator.h5')
        discriminator.load_weights('gooddiscriminator.h5')
    discriminator_on_generator = \
        generator_containing_discriminator(generator, discriminator)
    d_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)
    g_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)
    generator.compile(loss='binary_crossentropy', optimizer="SGD")
    discriminator_on_generator.compile(
        loss='binary_crossentropy', optimizer=g_optim)
    discriminator.trainable = True
    discriminator.compile(loss='binary_crossentropy', optimizer=d_optim)
    noise = np.zeros((BATCH_SIZE, 100))
    for epoch in range(epochs):
        print("Epoch is", epoch)
        print("Number of batches", int(X_train.shape[0]/BATCH_SIZE))
        for index in range(int(X_train.shape[0]/BATCH_SIZE)):
            for i in range(BATCH_SIZE):
                noise[i, :] = np.random.uniform(-1, 1, 100)
            image_batch = X_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]
            generated_images = generator.predict(noise, verbose=0)
            #print(generated_images.shape)
            if index % 20 == 0 and epoch % 10 == 0:
                image = combine_images(generated_images)
                image = image*127.5+127.5
                destpath = os.path.normpath(os.getcwd()+ "/logo-generated-images/"+str(epoch)+"_"+str(index)+".png")
                Image.fromarray(image.astype(np.uint8)).save(destpath)
            X = np.concatenate((image_batch, generated_images))
            y = [1] * BATCH_SIZE + [0] * BATCH_SIZE
            d_loss = discriminator.train_on_batch(X, y)
            print("batch %d d_loss : %f" % (index, d_loss))
            for i in range(BATCH_SIZE):
                noise[i, :] = np.random.uniform(-1, 1, 100)
            discriminator.trainable = False
            g_loss = discriminator_on_generator.train_on_batch(
                noise, [1] * BATCH_SIZE)
            discriminator.trainable = True
            print("batch %d g_loss : %f" % (index, g_loss))
            if epoch % 10 == 9:
                generator.save_weights('goodgenerator.h5', True)
                discriminator.save_weights('gooddiscriminator.h5', True)

def clean(image):
    for i in range(1, image.shape[0] - 1):
        for j in range(1, image.shape[1] - 1):
            if image[i][j] + image[i+1][j] + image[i][j+1] + image[i-1][j] + image[i][j-1] > 127 * 5:
                image[i][j] = 255
    return image
def generate(BATCH_SIZE):
    generator = generator_model()
    generator.compile(loss='binary_crossentropy', optimizer="SGD")
    generator.load_weights('goodgenerator.h5')
    noise = np.zeros((BATCH_SIZE, 100))
    a = np.random.uniform(-1, 1, 100)
    b = np.random.uniform(-1, 1, 100)
    grad = (b - a) / BATCH_SIZE
    for i in range(BATCH_SIZE):
        noise[i, :] = np.random.uniform(-1, 1, 100)
    generated_images = generator.predict(noise, verbose=1)
    #image = combine_images(generated_images)
    print(generated_images.shape)
    for image in generated_images:
        image = image[0]
        image = image*127.5+127.5
        Image.fromarray(image.astype(np.uint8)).save("dirty.png")
        Image.fromarray(image.astype(np.uint8)).show()
        clean(image)
        image = Image.fromarray(image.astype(np.uint8))
        image.show()        
        image.save("clean.png")


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--mode", type=str)
    parser.add_argument("--batch_size", type=int, default=128)
    parser.add_argument("--nice", dest="nice", action="store_true")
    parser.set_defaults(nice=False)
    args = parser.parse_args()
    return args

train(400, 10, False)

generate(1)

我尝试了GitHub存储库中的这段代码，以了解生成性对抗网络，但出现了以下错误。你能告诉我代码中的定义在哪里吗？请帮帮我

麻烦的线路：-

train(400, 10, False)

这就是错误：-

ValueError: It seems that you are using the Keras 2 and you are passing both `kernel_size` and `strides` as integer positional arguments. For safety reasons, this is disallowed. Pass `strides` as a keyword argument instead.

---

错误源于模型中每次添加的

Conv2D

层。您需要更改代码中的行

model.add(Convolution2D(64, 5, 5, border_mode='same'))

例如（取决于你到底想要什么）

注意，我在这里明确地命名了参数

strips

，因为错误告诉我应该将其作为关键字参数传递

model.add(Conv2D(64,kernel_size=5,strides=2,padding='same'))