Reach score 719
2019/02/11 00:01 投稿
| 【Python】【Keras】【GAN】指定ディレクトリ下の画像の学習ができない |
| 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](https://github.com/eriklindernoren/Keras-GAN/tree/master/wgan_gp)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 |
| 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) |
| ``` |
