質問編集履歴
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誤字の修正
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dim = K.int_shape(z_mean)[1]
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# by default, random_normal has mean=0 and std=1.0
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epsilon = K.random_normal(shape=(batch, dim))
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return z_mean + K.exp(0.5 * z_log_var) * epsilon
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encoder, decoder = models
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#x_test, y_test = data
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os.makedirs(model_name, exist_ok=True)
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エラー内容の変更
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Python3 ValueError
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Python3 VInvalidArgumentError (see above for traceback): Incompatible shapesのエラー解消方法について
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KerasのVAEを自作データセットで実装中、以下のようなエラーが出ました。該当の部分を見ても、どう修正したら良いか分からず、
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KerasのVAEを自作データセットで実装中、以下のようなエラーが出ました。該当の部分を見ても、どう修正したら良いか分からず、質問させて頂きました。
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InvalidArgumentError Traceback (most recent call last)
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InvalidArgumentError: Incompatible shapes: [12800] vs. [450]
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### 該当のソースコード
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from keras.layers import Dense, Input
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from keras.layers import Conv2D, Flatten, Lambda
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from keras.layers import Reshape, Conv2DTranspose
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from keras.models import Model
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from keras.losses import mse, binary_crossentropy
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from keras.utils import plot_model
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from keras import backend as K
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from keras import optimizers
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import numpy as np
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import matplotlib.pyplot as plt
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import argparse
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import tensorflow as tf
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import random as rn
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import os
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import easydict
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import warnings
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warnings.filterwarnings('ignore')
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%matplotlib inline
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def sampling(args):
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z_mean, z_log_var = args
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batch = K.shape(z_mean)[0]
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dim = K.int_shape(z_mean)[1]
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# by default, random_normal has mean=0 and std=1.0
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epsilon = K.random_normal(shape=(batch, dim))
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return z_mean + K.exp(0.5 * z_log_var) * epsilon
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def plot_results(models,
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data,
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batch_size=50,
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model_name="vae_mnist"):
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encoder, decoder = models
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#x_test, y_test = data
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os.makedirs(model_name, exist_ok=True)
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filename = os.path.join(model_name, "vae_mean.png")
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z_mean, _, _ = encoder.predict(x_test,
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batch_size=batch_size)
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plt.figure(figsize=(12, 10))
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plt.scatter(z_mean[:, 0], z_mean[:, 1])
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plt.colorbar()
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plt.xlabel("z[0]")
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plt.ylabel("z[1]")
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plt.savefig(filename)
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plt.show()
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filename = os.path.join(model_name, "digits_over_latent.png")
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n = 30
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digit_size = 224
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figure = np.zeros((digit_size * n, digit_size * n))
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grid_x = np.linspace(-4, 4, n)
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grid_y = np.linspace(-4, 4, n)[::-1]
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for i, yi in enumerate(grid_y):
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for j, xi in enumerate(grid_x):
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z_sample = np.array([[xi, yi]])
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x_decoded = decoder.predict(z_sample)
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digit = x_decoded[0].reshape(digit_size, digit_size)
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figure[i * digit_size: (i + 1) * digit_size,
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j * digit_size: (j + 1) * digit_size] = digit
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plt.figure(figsize=(10, 10))
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start_range = digit_size // 2
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end_range = n * digit_size + start_range + 1
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pixel_range = np.arange(start_range, end_range, digit_size)
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sample_range_x = np.round(grid_x, 1)
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sample_range_y = np.round(grid_y, 1)
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plt.xticks(pixel_range, sample_range_x)
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plt.yticks(pixel_range, sample_range_y)
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plt.xlabel("z[0]")
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plt.ylabel("z[1]")
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plt.imshow(figure, cmap='Greys_r')
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plt.savefig(filename)
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plt.show()
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x_train_tom = np.load('./folder_a.npy')
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x_test_tom = np.load('./folder_b.npy')
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image_size = x_train_tom.shape[1]
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x_train = np.reshape(x_train_tom, [-1, image_size, image_size, 1])
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x_test = np.reshape(x_test_tom, [-1, image_size, image_size, 1])
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x_train = x_train.astype('float32') / 255
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x_test = x_test.astype('float32') / 255
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print(x_train.shape,x_test.shape)
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input_shape = (image_size,image_size, 1)
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batch_size = 50
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kernel_size = 3
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filters = 16
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latent_dim = 2
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epochs = 50
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inputs = Input(shape=input_shape, name='encoder_input')
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filters *= 2
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x = Conv2D(filters=filters,
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kernel_size=kernel_size,
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activation='relu',
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strides=2,
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padding='same')(x)
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shape = K.int_shape(x)
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x = Flatten()(x)
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x = Dense(64, activation='relu')(x)
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z_mean = Dense(latent_dim, name='z_mean')(x)
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z_log_var = Dense(latent_dim, name='z_log_var')(x)
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z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])
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encoder = Model(inputs, [z_mean, z_log_var, z], name='encoder')
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encoder.summary()
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plot_model(encoder, to_file='vae_cnn_encoder.png', show_shapes=True)
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latent_inputs = Input(shape=(latent_dim,), name='z_sampling')
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x = Dense(shape[1] * shape[2] * shape[3], activation='relu')(latent_inputs)
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x = Reshape((shape[1], shape[2], shape[3]))(x)
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for i in range(4):
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x = Conv2DTranspose(filters=filters,
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kernel_size=kernel_size,
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activation='relu',
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strides=2,
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padding='same')(x)
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filters //= 2
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outputs = Conv2DTranspose(filters=1,
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kernel_size=kernel_size,
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activation='sigmoid',
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padding='same',
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name='decoder_output')(x)
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decoder = Model(latent_inputs, outputs, name='decoder')
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decoder.summary()
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plot_model(decoder, to_file='vae_cnn_decoder.png', show_shapes=True)
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outputs = decoder(encoder(inputs)[2])
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vae = Model(inputs, outputs, name='vae')
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def plot_history(history):
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plt.plot(history.history['loss'])
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plt.plot(history.history['val_loss'])
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plt.title('model accuracy')
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plt.xlabel('epoch')
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plt.ylabel('accuracy')
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356
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plt.legend(['acc', 'val_acc'], loc='lower right')
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plt.show()
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plt.plot(history.history['loss'])
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plt.plot(history.history['val_loss'])
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plt.title('model loss')
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368
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+
plt.xlabel('epoch')
|
|
370
|
+
|
|
371
|
+
plt.ylabel('loss')
|
|
372
|
+
|
|
373
|
+
plt.legend(['loss', 'val_loss'], loc='lower right')
|
|
374
|
+
|
|
375
|
+
plt.savefig('loss.png') # -----(2)
|
|
376
|
+
|
|
377
|
+
plt.show()
|
|
378
|
+
|
|
379
|
+
|
|
380
|
+
|
|
381
|
+
|
|
382
|
+
|
|
383
|
+
|
|
384
|
+
|
|
385
|
+
if __name__ == '__main__':
|
|
386
|
+
|
|
387
|
+
args = easydict.EasyDict({
|
|
388
|
+
|
|
389
|
+
"batchsize": 50,
|
|
390
|
+
|
|
391
|
+
"epoch": 50,
|
|
392
|
+
|
|
393
|
+
"gpu": 0,
|
|
394
|
+
|
|
395
|
+
"out": "result",
|
|
396
|
+
|
|
397
|
+
"resume": False,
|
|
398
|
+
|
|
399
|
+
"unit": 1000
|
|
400
|
+
|
|
401
|
+
})
|
|
402
|
+
|
|
403
|
+
models = (encoder, decoder)
|
|
404
|
+
|
|
405
|
+
|
|
406
|
+
|
|
407
|
+
|
|
408
|
+
|
|
409
|
+
os.environ['PYTHONHASHSEED'] = '0'
|
|
410
|
+
|
|
411
|
+
np.random.seed(5)
|
|
412
|
+
|
|
413
|
+
rn.seed(5)
|
|
414
|
+
|
|
415
|
+
|
|
416
|
+
|
|
417
|
+
config = tf.ConfigProto(
|
|
418
|
+
|
|
419
|
+
gpu_options=tf.GPUOptions(
|
|
420
|
+
|
|
421
|
+
visible_device_list="0,1",
|
|
422
|
+
|
|
423
|
+
allow_growth=True
|
|
424
|
+
|
|
425
|
+
)
|
|
426
|
+
|
|
427
|
+
)
|
|
428
|
+
|
|
429
|
+
|
|
430
|
+
|
|
431
|
+
tf.set_random_seed(5)
|
|
432
|
+
|
|
433
|
+
sess = tf.Session(graph=tf.get_default_graph(), config=config)
|
|
434
|
+
|
|
435
|
+
K.set_session(sess)
|
|
436
|
+
|
|
437
|
+
|
|
438
|
+
|
|
439
|
+
|
|
440
|
+
|
|
441
|
+
reconstruction_loss = binary_crossentropy(K.flatten(inputs),
|
|
442
|
+
|
|
443
|
+
K.flatten(outputs))
|
|
444
|
+
|
|
445
|
+
|
|
446
|
+
|
|
447
|
+
reconstruction_loss *= image_size * image_size
|
|
448
|
+
|
|
449
|
+
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
|
|
450
|
+
|
|
451
|
+
kl_loss = K.sum(kl_loss, axis=-1)
|
|
452
|
+
|
|
453
|
+
kl_loss *= -0.5
|
|
454
|
+
|
|
455
|
+
vae_loss = K.mean(reconstruction_loss + kl_loss)
|
|
456
|
+
|
|
457
|
+
vae.add_loss(vae_loss)
|
|
458
|
+
|
|
459
|
+
Adam = optimizers.Adam(lr=0.0005)
|
|
460
|
+
|
|
461
|
+
vae.compile(optimizer=Adam)
|
|
462
|
+
|
|
463
|
+
vae.summary()
|
|
464
|
+
|
|
465
|
+
plot_model(vae, to_file='vae_cnn.png', show_shapes=True)
|
|
466
|
+
|
|
467
|
+
|
|
468
|
+
|
|
469
|
+
history = vae.fit(x_train,
|
|
470
|
+
|
|
471
|
+
epochs=epochs,
|
|
472
|
+
|
|
473
|
+
batch_size=batch_size,
|
|
474
|
+
|
|
475
|
+
validation_data=(x_test, None))
|
|
476
|
+
|
|
477
|
+
|
|
478
|
+
|
|
479
|
+
open('vae_cnn.json', "w").write(vae.to_json())
|
|
480
|
+
|
|
481
|
+
vae.save('vae_cnn.h5')
|
|
482
|
+
|
|
483
|
+
|
|
484
|
+
|
|
485
|
+
plot_results(models, data, batch_size=batch_size, model_name="vae_cnn")
|
|
486
|
+
|
|
487
|
+
plot_history(history)
|
|
488
|
+
|
|
489
|
+
|
|
490
|
+
|
|
491
|
+
#補足
|
|
492
|
+
|
|
493
|
+
kerasやtensorflowのバージョンが問題になっている場合もあるようですが、上手くいった人のバージョンに変更しても変化がありませんでした。
|
|
494
|
+
|
|
495
|
+
コードが長くてお手数をおかけしますが、アドバイス頂けると幸いです。どうぞよろしくお願い致します。
|