ValueError: It seems that you are using the Keras 2 and you are passing both `kernel_size` and `strides` as integer positional arguments
ValueError: It seems that you are using the Keras 2 and you are passing both `kernel_size` and `strides` as integer positional arguments
我是一名计算机专业的本科生，在读第四学期，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)
我正在尝试 this GitHub 存储库中的 GAN 代码，以了解生成对抗网络，但出现以下错误。你能告诉我代码中哪里提供了定义吗？请帮助我！
麻烦的线路：-
train(400, 10, False)
这是错误：-
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.
错误是由模型中每次添加 Conv2D 层引起的。您需要更改代码中的行
model.add(Convolution2D(64, 5, 5, border_mode='same'))
类似的东西（取决于你到底想要什么）
model.add(Conv2D(64,kernel_size=5,strides=2,padding='same'))
请注意，我在这里明确命名了参数 strides，因为错误提示我应该将其作为关键字参数传递。
我是一名计算机专业的本科生，在读第四学期，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)
我正在尝试 this GitHub 存储库中的 GAN 代码，以了解生成对抗网络，但出现以下错误。你能告诉我代码中哪里提供了定义吗？请帮助我！
麻烦的线路：-
train(400, 10, False)
这是错误：-
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.
错误是由模型中每次添加 Conv2D 层引起的。您需要更改代码中的行
model.add(Convolution2D(64, 5, 5, border_mode='same'))
类似的东西（取决于你到底想要什么）
model.add(Conv2D(64,kernel_size=5,strides=2,padding='same'))
请注意，我在这里明确命名了参数 strides，因为错误提示我应该将其作为关键字参数传递。