読ませようとした画像はどんなものでしょうか。1×1のグレースケール？
色々いじったを、具体的に示してください。
お世話になります
機械(深層)学習の超初心者です
現在 WGAN-GPで カラー画像を (Colab上で) 学習させようとチャレンジしております
ここのコードを使っております
画像は読み込んでいるようなのですが 以下のエラーが出て 実行できません
Traceback (most recent call last): File "wgan_gp_3.py", line 270, in <module> wgan.train(epochs=30000, batch_size=8, sample_interval=100) File "wgan_gp_3.py", line 234, in train [valid, fake, dummy]) File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1217, in train_on_batch outputs = self.train_function(ins) File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__ return self._call(inputs) File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 2675, in _call fetched = self._callable_fn(*array_vals) File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1439, in __call__ run_metadata_ptr) File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__ c_api.TF_GetCode(self.status.status)) tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [32,1,1,1] vs. [8,3,256,256] [[{{node random_weighted_average_1/mul_1}}]] [[{{node loss/model_2_loss/Mean_3}}]]
変更部分コード
Python
from __future__ import print_function, division from keras.datasets import mnist from keras.layers.merge import _Merge from keras.layers import Input, Dense, Reshape, Flatten, Dropout from keras.layers import BatchNormalization, Activation, ZeroPadding2D from keras.layers.advanced_activations import LeakyReLU from keras.layers.convolutional import UpSampling2D, Conv2D from keras.models import Sequential, Model from keras.optimizers import RMSprop from functools import partial from keras.preprocessing.image import load_img, img_to_array,save_img , array_to_img from tensorflow.python.keras.preprocessing.image import ImageDataGenerator import keras.backend as K import matplotlib.pyplot as plt import glob import sys import numpy as np class WGANGP(): def __init__(self): self.img_rows = 256 self.img_cols = 256 self.channels = 3 self.img_shape = (self.img_rows, self.img_cols, self.channels) self.latent_dim = 100 data_dir = '/content/drive/My Drive/images/' batch_size = 8 gen = ImageDataGenerator(rescale=1/127.5, samplewise_center=True) iters = gen.flow_from_directory( directory=data_dir, classes=['test'], class_mode=None, color_mode='rgb', target_size=self.img_shape[:2], batch_size=batch_size, shuffle=True ) self.x_train_batch=next(iters) # Following parameter and optimizer set as recommended in paper self.n_critic = 5 optimizer = RMSprop(lr=0.00005) # Build the generator and critic self.generator = self.build_generator() self.critic = self.build_critic() #------------------------------- # Construct Computational Graph # for the Critic #------------------------------- # Freeze generator's layers while training critic self.generator.trainable = False # Image input (real sample) real_img = Input(shape=self.img_shape) # Noise input z_disc = Input(shape=(self.latent_dim,)) # Generate image based of noise (fake sample) fake_img = self.generator(z_disc) # Discriminator determines validity of the real and fake images fake = self.critic(fake_img) valid = self.critic(real_img) # Construct weighted average between real and fake images interpolated_img = RandomWeightedAverage()([real_img, fake_img]) # Determine validity of weighted sample validity_interpolated = self.critic(interpolated_img) # Use Python partial to provide loss function with additional # 'averaged_samples' argument partial_gp_loss = partial(self.gradient_penalty_loss, averaged_samples=interpolated_img) partial_gp_loss.__name__ = 'gradient_penalty' # Keras requires function names self.critic_model = Model(inputs=[real_img, z_disc], outputs=[valid, fake, validity_interpolated]) self.critic_model.compile(loss=[self.wasserstein_loss, self.wasserstein_loss, partial_gp_loss], optimizer=optimizer, loss_weights=[1, 1, 10]) #------------------------------- # Construct Computational Graph # for Generator #------------------------------- # For the generator we freeze the critic's layers self.critic.trainable = False self.generator.trainable = True # Sampled noise for input to generator z_gen = Input(shape=(100,)) # Generate images based of noise img = self.generator(z_gen) # Discriminator determines validity valid = self.critic(img) # Defines generator model self.generator_model = Model(z_gen, valid) self.generator_model.compile(loss=self.wasserstein_loss, optimizer=optimizer) def build_generator(self): model = Sequential() model.add(Dense(128 * 64 * 64, activation="relu", input_dim=self.latent_dim)) model.add(Reshape((64, 64, 128))) model.add(UpSampling2D()) model.add(Conv2D(128, kernel_size=4, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(UpSampling2D()) model.add(Conv2D(64, kernel_size=4, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(Conv2D(self.channels, kernel_size=4, padding="same")) model.add(Activation("tanh")) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img) def train(self, epochs, batch_size, sample_interval=50): # Adversarial ground truths valid = -np.ones((batch_size, 1)) fake = np.ones((batch_size, 1)) dummy = np.zeros((batch_size, 1)) # Dummy gt for gradient penalty for epoch in range(epochs): for _ in range(self.n_critic): # --------------------- # Train Discriminator # --------------------- # Select a random batch of images X_train = self.x_train_batch X_train = ((np.array(X_train, dtype=np.float32) - 127.5) / 127.5) imgs = X_train # Sample generator input noise = np.random.normal(0, 1, (batch_size, self.latent_dim)) # Train the critic d_loss = self.critic_model.train_on_batch([imgs, noise], [valid, fake, dummy]) # --------------------- # Train Generator # --------------------- g_loss = self.generator_model.train_on_batch(noise, valid) # Plot the progress print ("%d [D loss: %f] [G loss: %f]" % (epoch, d_loss[0], g_loss)) # If at save interval => save generated image samples if epoch % sample_interval == 0: self.sample_images(epoch)
試したこと
色々と 数値を変えて試してみましたが 解決に至りませんでした
どなたか わかる方 ご教示 よろしく お願い致します
追記：
画像は 256 x 256 のカラー画像です (538枚)
他 ^C が表示されたコード
Python
class RandomWeightedAverage(_Merge): """Provides a (random) weighted average between real and generated image samples""" def _merge_function(self, inputs): alpha = K.random_uniform((256, 1, 1, 1)) return (alpha * inputs[0]) + ((1 - alpha) * inputs[1])
Python
def build_critic(self): model = Sequential() model.add(Conv2D(256, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(512, kernel_size=3, strides=2, padding="same")) model.add(ZeroPadding2D(padding=((0,1),(0,1)))) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(1024, kernel_size=3, strides=2, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(2048, kernel_size=3, strides=1, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(1)) model.summary() img = Input(shape=self.img_shape) validity = model(img) return Model(img, validity)
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