層“conv2d_transpose_4”的輸入0與層不兼容：預期ndim=4，發現ndim=2。收到的完整形狀：（無，100）

我正在嘗試開發 GAN，我已經創建了生成器和鑒別器，現在我正在嘗試訓練它。我正在使用 Mnist 資料集，但我計劃使用更多。問題是，當我訓練它時，我得到了這個錯誤：Input 0 of layer "conv2d_transpose_4" is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: (None, 100)

我真的不知道問題出在網路還是用于訓練 GAN 的資料中，有人能告訴我應該如何訓練它或問題出在哪里嗎？

進口：

import tensorflow
import keras
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Flatten, Input, BatchNormalization, 
LeakyReLU, Reshape
from keras.layers import Conv2D, Conv2DTranspose, MaxPooling2D
from tensorflow.keras.optimizers import Adam
from keras import backend as K
from keras.utils import np_utils

from keras.datasets import mnist

import numpy as np
import matplotlib.pyplot as plt
import os
import cv2

發電機：

def generator():
   model = Sequential()
   model.add(Conv2DTranspose(32, (3,3), strides=(2, 
   2), activation='relu', use_bias=False, 
   input_shape=img_shape))
   model.add(BatchNormalization(momentum=0.3))
   model.add(Conv2DTranspose(128,(3,3),strides= 
   (2,2), activation='relu', padding='same', 
   use_bias=False)) 
   model.add(MaxPooling2D(pool_size=(2, 2)))
   model.add(LeakyReLU(alpha=0.2))

   model.add(Conv2DTranspose(128,(3,3),strides= 
   (2,2), activation='relu', padding='same', 
   use_bias=False))
   model.add(MaxPooling2D(pool_size=(2, 2)))
   model.add(Dropout(0.5))
   model.add(BatchNormalization(momentum=0.3))
   model.add(LeakyReLU(alpha=0.2))

   model.add(Conv2DTranspose(128,(3,3),strides= 
   (2,2), activation='relu', padding='same', 
   use_bias=False))
   model.add(BatchNormalization())
   model.add(Dense(512, 
   activation=LeakyReLU(alpha=0.2)))
   model.add(BatchNormalization(momentum=0.7))

   model.build()
   model.summary()
   return model

鑒別器：

def discriminator():
   model = Sequential()
   model.add(Conv2D(32, (5,5), strides=(2, 2), 
   activation='relu', use_bias=False, 
   input_shape=img_shape))
   model.add(BatchNormalization(momentum=0.3))
   model.add(Conv2D(64,(5,5),strides=(2,2), 
   activation='relu', padding='same', 
   use_bias=False))
   model.add(MaxPooling2D(pool_size=(2, 2)))
   model.add(LeakyReLU(alpha=0.2))

   model.add(Conv2D(64,(5,5),strides=(2,2), 
   activation='relu', padding='same', 
   use_bias=False))
   model.add(Dropout(0.5))
   model.add(BatchNormalization(momentum=0.3))
   model.add(LeakyReLU(alpha=0.2))

   model.add(Dense(512, 
   activation=LeakyReLU(alpha=0.2)))
   model.add(Flatten())
   model.add(BatchNormalization(momentum=0.7))
   model.add(Dense(1, activation='sigmoid'))

   model.build()
   model.summary()

   return model

火車功能：

def train(epochs, batch_size, save_interval):

(x_train, _), (_, _) = mnist.load_data()

x_train = (x_train.astype(np.float32) - 127.5) / 127.5
x_train = np.expand_dims(x_train, axis=3) 
half_batch = int(batch_size / 2)

for epoch in range(epochs):
    idx = np.random.randint(0, x_train.shape[0], half_batch)
    imgs = x_train[idx]

    noise = np.random.normal(0, 1, (half_batch, 100))

    gen_imgs = generator.predict(noise)

    d_loss_real = discriminator.train_on_batch(imgs, np.ones((half_batch, 1)))
    d_loss_fake = discriminator.train_on_batch(gen_imgs, np.zeros((half_batch, 1)))

    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) 
    noise = np.random.normal(0, 1, (batch_size, 100)) 
    valid_y = np.array([1] * batch_size)
    g_loss = combined.train_on_batch(noise, valid_y)

    print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
    if epoch % save_interval == 0:
        save_imgs(epoch)

使用的資料：

img_rows = 28
img_cols = 28
channels = 1
img_shape = (img_rows, img_cols, channels)

optimizer = Adam(0.0002, 0.5)
discriminator = discriminator()
discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])

generator = generator()
generator.compile(loss='binary_crossentropy', 
optimizer=optimizer)

z = Input(shape=(100,))
img = generator(z) #error

discriminator.trainable = False  

valid = discriminator(img)

combined = Model(z, valid)
combined.compile(loss='binary_crossentropy', 
optimizer=optimizer)


train(epochs=100000, batch_size=32, 
save_interval=10)

generator.save('generator_model.h5')

uj5u.com熱心網友回復：

問題來自模型的第一Flatten層Discriminator，它將你的 n 維張量轉換為一維張量。由于MaxPooling2D圖層不能使用一維張量，因此您會看到該錯誤。如果洗掉它，它應該可以作業：

def discriminator():
   model = Sequential()
   model.add(Conv2D(32, (5,5), strides=(2, 2), 
   activation='relu', use_bias=False, 
   input_shape=img_shape))
   model.add(BatchNormalization(momentum=0.3))
   model.add(Conv2D(64,(5,5),strides=(2,2), 
   activation='relu', padding='same', 
   use_bias=False))
   model.add(MaxPooling2D(pool_size=(2, 2)))
   model.add(LeakyReLU(alpha=0.2))

   model.add(Conv2D(64,(5,5),strides=(2,2), 
   activation='relu', padding='same', 
   use_bias=False))
   model.add(Dropout(0.5))
   model.add(BatchNormalization(momentum=0.3))
   model.add(LeakyReLU(alpha=0.2))

   model.add(Flatten())
   model.add(Dense(512, 
   activation=LeakyReLU(alpha=0.2)))
   model.add(BatchNormalization(momentum=0.7))
   model.add(Dense(1, activation='sigmoid'))

   model.build()
   model.summary()

   return model

更新 1：嘗試Generator像這樣重寫您的模型：

def generator():
  model = Sequential()

  model = tf.keras.Sequential()
  model.add(Dense(7*7*256, use_bias=False, input_shape=(100,)))
  model.add(BatchNormalization())
  model.add(LeakyReLU())

  model.add(Reshape((7, 7, 256)))
  assert model.output_shape == (None, 7, 7, 256)  # Note: None is the batch size

  model.add(Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
  assert model.output_shape == (None, 7, 7, 128)
  model.add(BatchNormalization())
  model.add(LeakyReLU())

  model.add(Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
  assert model.output_shape == (None, 14, 14, 64)
  model.add(BatchNormalization())
  model.add(LeakyReLU())

  model.add(Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
  assert model.output_shape == (None, 28, 28, 1)

  model.summary()
  return model

然后它應該可以作業，但是您絕對應該閱讀本

標籤：张量流 机器学习 喀拉斯 深度学习 生成对抗网络

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