Q&A
前提・実現したいこと
Pythonで自作のデータセットを使ってVAEのコードを書いています。
train画像を準備する部分で、以下のようなエラーメッセージが出てしまいました。
このエラーが出た時はいつもコードを一度消して再度入力するとエラーが解消されることがほとんどなのですが、今回は何度再入力しても、エラーが消えないため、質問をさせていただきました。
お手数をお掛けしますが、解決策についてご存じの方がいらっしゃいましたら、ご教示いただけますと幸いです。
発生している問題・エラーメッセージ
File "<ipython-input-14-7dac0b4001f0>", line 111 X = [] ^ SyntaxError: invalid syntax
該当のソースコード
from __future__ import absolute_import from __future__ import division from __future__ import print_function from keras.layers import Lambda, Input, Dense from keras.models import Model from keras.models import Sequential, model_from_json from keras.losses import mse, binary_crossentropy from keras.layers import Conv2D, Flatten, Lambda from keras.layers import Reshape, Conv2DTranspose from keras.utils import plot_model, np_utils from keras.utils import plot_model from keras.callbacks import Callback, EarlyStopping, TensorBoard, ModelCheckpoint from keras import optimizers from keras import backend as K from keras.preprocessing.image import array_to_img, img_to_array,load_img from keras.preprocessing.image import ImageDataGenerator import numpy as np import matplotlib.pyplot as plt import argparse import os import re import glob import random as rn import tensorflow as tf import cv2 import easydict from PIL import Image #from google.colab.patches import cv2_imshow import warnings warnings.filterwarnings('ignore') %matplotlib inline def sampling(args): z_mean, z_log_var = args batch = K.shape(z_mean)[0] dim = K.int_shape(z_mean)[1] # by default, random_normal has mean=0 and std=1.0 epsilon = K.random_normal(shape=(batch, dim)) return z_mean + K.exp(0.5 * z_log_var) * epsilon #def plot_results(models, #data, #batch_size=500,#128, #model_name="vae_OCT"): """Plots labels and MNIST digits as function of 2-dim latent vector # Arguments models (tuple): encoder and decoder models data (tuple): test data and label batch_size (int): prediction batch size encoder, decoder = models x_test = data #, y_test削除 os.makedirs(model_name, exist_ok=True) model_name (string): which model is using this function """ filename = os.path.join('.', "vae_mean.png") # display a 2D plot of the digit classes in the latent space z_mean, _, _ = encoder.predict(x_test, batch_size=batch_size) #plt.figure(figsize=(12, 10)) #plt.scatter(z_mean[:, 0], z_mean[:, 1])#c=y_test削除 #plt.colorbar() #plt.xlabel("z[0]") #plt.ylabel("z[1]") #plt.savefig(filename) #plt.show() #filename = os.path.join(model_name, "digits_over_latent.png") # display a 30x30 2D manifold of digits #n = 30 #digit_size = 496 うまく動かないので一旦プロット部分はすべてコメントアウト #digit_size_width = 512 #width追加 #digit_size_height = 496 #height追加 #figure = np.zeros((digit_size_width * n, digit_size_height * n))#width, height追加 # linearly spaced coordinates corresponding to the 2D plot # of digit classes in the latent space #grid_x = np.linspace(-4, 4, n) #grid_y = np.linspace(-4, 4, n)[::-1] #for i, yi in enumerate(grid_y): #for j, xi in enumerate(grid_x): #z_sample = np.array([[xi, yi]]) #x_decoded = decoder.predict(z_sample) #digit = x_decoded[0].reshape(digit_size_width, digit_size_height)#width, height追加 #figure[i * digit_size_width: (i + 1) * digit_size_height,#width, height追加 #j * digit_size_width: (j + 1) * digit_size_height] = digit#width, height追加 #plt.figure(figsize=(10, 10)) #start_range = digit_size // 2 #end_range = n * digit_size + start_range + 1 #pixel_range = np.arange(start_range, end_range, digit_size) #sample_range_x = np.round(grid_x, 1) #sample_range_y = np.round(grid_y, 1) #plt.xticks(pixel_range, sample_range_x) #plt.yticks(pixel_range, sample_range_y) #plt.xlabel("z[0]") #plt.ylabel("z[1]") #plt.imshow(figure, cmap='Greys_r') #plt.savefig(filename) #plt.show() #original dataset #train filenames = glob.glob("./train/NORMAL_train_dataset/*.jpeg") X = [] for filename in filenames: img = img_to_array(load_img( filename, color_mode = "grayscale", target_size=(512,496)) X.append(img) x_train = np.asarray(X) #test filenames = glob.glob("./validation/*.jpeg") x_test = [] for filename in filenames: img = img_to_array(load_img( filename, color_mode = "grayscale", target_size=(512,496)) x_test.append(img) x_test = np.asarray(x_test) image_size = x_train.shape[1] original_dim = 512 * 496 *1 #3削除 x_train = np.reshape(x_train, [-1, original_dim,1])# x_train = np.reshape(x_train, [-1, original_dim]) x_test = np.reshape(x_test, [-1, original_dim,1])# x_test = np.reshape(x_test, [-1, original_dim]) x_train = x_train.astype('float32') / 255 x_test = x_test.astype('float32') / 255 #train_generator = train_datagen.flow(x_train)#追記。generator作成 #test_generator = test_datagen.flow(x_test)#追記。generator作成 print(x_train.shape) print(x_test.shape) # network parameters input_shape = (512, 496, 1)# (original_dim,) kernel_size = 3 filters = 16 #intermediate_dim = 512 batch_size = 500#128 latent_dim = 2# Dimensionality of the latent space: a plane 潜在空間の次元数：平面 https://fisproject.jp/2018/09/vae-with-python-keras/#vae-with-keras epochs = 5#1#50 # build encoder model inputs = Input(shape=input_shape, name='encoder_input') x = inputs for i in range(4): filters *= 2 x = Conv2D(filters=filters,kernel_size=kernel_size,activation='relu',strides=2,padding='same')(x) # shape info needed to build decoder model これは画像のshapeとって割る時とかに結構使う. ちなみにtensorflowでのみ動作します. https://www.mathgram.xyz/entry/keras/backend shape = K.int_shape(x) # use reparameterization trick to push the sampling out as input # note that "output_shape" isn't necessary with the TensorFlow backend z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var]) # instantiate encoder model encoder = Model(inputs, [z_mean, z_log_var, z], name='encoder') encoder.summary() plot_model(encoder, to_file='vae_mlp_encoder.png', show_shapes=True) # build decoder model latent_inputs = Input(shape=(latent_dim,), name='z_sampling') x = Dense(shape[1] * shape[2] * shape[3], activation='relu')(latent_inputs) x = Reshape((shape[1], shape[2], shape[3]))(x) for i in range(4): x = Conv2DTranspose(filters=filters, kernel_size=kernel_size, activation='relu', strides=2, padding='same')(x) filters //= 2 outputs = Conv2DTranspose(filters=1, kernel_size=kernel_size, activation='sigmoid', padding='same', name='decoder_output')(x) # instantiate decoder model decoder = Model(latent_inputs, outputs, name='decoder') decoder.summary() plot_model(decoder, to_file='vae_mlp_decoder.png', show_shapes=True) # instantiate VAE model outputs = decoder(encoder(inputs)[2]) vae = Model(inputs, outputs, name='vae_mlp') if __name__ == '__main__': args = easydict.EasyDict({ "batchsize": 500,#40, "epoch": 1,#50, #"gpu": 0, "out": "result", "resume": False, #"unit": 1000 }) #parser = argparse.ArgumentParser() parserがうまくうごかないため削除 #help_ = "Load h5 model trained weights" #parser.add_argument("-w", "--weights", help=help_) #help_ = "Use mse loss instead of binary cross entropy (default)" #parser.add_argument("-m", #"--mse", #help=help_, action='store_true') #args = parser.parse_args() models = (encoder, decoder) data = (x_test)#, y_test削除 # VAE loss = mse_loss or xent_loss + kl_loss #if args.mse: #reconstruction_loss = mse(inputs, outputs) #else: reconstruction_loss = binary_crossentropy(inputs, outputs) reconstruction_loss *= original_dim kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var) kl_loss = K.sum(kl_loss, axis=-1) kl_loss *= -0.5 vae_loss = K.mean(reconstruction_loss + kl_loss) vae.add_loss(vae_loss) vae.compile(optimizer='adam') vae.summary() plot_model(vae, to_file='vae_mlp.png', show_shapes=True) callbacks = [] callbacks.append(ModelCheckpoint(filepath="model.ep{epoch:02d}.h5"))# 各epochでのモデルの保存 callbacks.append(EarlyStopping(monitor='val_loss', patience=0, verbose=1)) callbacks.append(LearningRateScheduler(lambda ep: float(1e-3 / 3 ** (ep * 4 // MAX_EPOCH)))) callbacks.append(CSVLogger("history.csv")) #if args.weights: #vae.load_weights(args.weights) #else: # train the autoencoder history = vae.fit(x_train, epochs=epochs, batch_size=batch_size, validation_data=(x_test, None), callbacks=callbacks) score = model.evaluate(x_test, verbose=0)#y_test削除 print('Test loss:', score[0]) print('Test accuracy:', score[1]) plt.plot(history.history["acc"], label="acc", ls="-", marker="o") plt.plot(history.history["val_acc"], label="val_acc", ls="-", marker="x") plt.ylabel("accuracy") plt.xlabel("epoch") plt.legend(loc="best") plt.show()
補足情報（FW/ツールのバージョンなど）
ubuntuで、anacondaの仮想環境を使って実装しています。
回答1件
0
ベストアンサー
なにしろ当該箇所がコード上に存在しないので困ったものですが、カーネルリスタートしてRun Allで解消しませんか？
投稿2019/05/12 11:34
総合スコア30935
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