質問編集履歴
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あるモデルを作って学習させた後、そのモデルの一部を変更して再度学習させたい時に、どのようなプログラムを書けば最初に学習させた時の重みを使いながら再度学習することができるでしょうか。
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現在、CVAEのプログラムを作ろうとしているところで、止まってしまっています。
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プログラムの例などがあると、幸いです。
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よろしくお願いします。
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よろしくお願いします。
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```python
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from __future__ import print_function
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.stats import norm
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import time
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from collections import Counter
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from keras.layers import Input, Dense, Lambda, Concatenate
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from keras.models import Model
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from keras import backend as K
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from keras import metrics
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from keras.datasets import mnist
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from keras import utils
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batch_size = 100
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original_dim = 784
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latent_dim = 10
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intermediate_dim = 256
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epochs = 25
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cat_dim = 10
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epsilon_std = 1.0
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x = Input(shape=(original_dim,))
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h = Dense(intermediate_dim, activation='relu')(x)
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z_mean = Dense(latent_dim)(h)
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z_log_var = Dense(latent_dim)(h)
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def sampling(args):
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z_mean, z_log_var = args
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epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0.,
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stddev=epsilon_std)
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return z_mean + K.exp(z_log_var / 2) * epsilon
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def vae_loss(x, x_decoded_mean):
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x = K.flatten(x)
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x_decoded_mean = K.flatten(x_decoded_mean)
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xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)
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kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
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return K.mean(xent_loss + kl_loss)
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z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
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# ラベルありデータのラベルを入力
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ly = Input(shape=(cat_dim,))
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merge = Concatenate()([z, ly])
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decoder_h = Dense(intermediate_dim, activation='relu')
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decoder_mean = Dense(original_dim, activation='sigmoid')
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h_decoded = decoder_h(merge)
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x_decoded_mean = decoder_mean(h_decoded)
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# ラベルありの時のモデル
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labeled_M2 = Model([x,ly], x_decoded_mean)
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labeled_M2.compile(optimizer='rmsprop', loss=vae_loss)
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# データ整形
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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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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x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
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x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
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# トレーニングデータを100個選択
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random_Num = np.random.randint(0,10000)
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np.random.seed(random_Num)
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np.random.shuffle(x_test)
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np.random.seed(random_Num)
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np.random.shuffle(y_test)
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x_realtest = x_test[100:] # テストデータ
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y_realtest = y_test[100:] # テストデータのラベル
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x_test = np.delete(x_test, range(100,10000), axis=0)
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y_test = np.delete(y_test, range(100,10000), axis=0)
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# 教師なしデータ,教師ありデータ,テスト用データのラベルを,それぞれone-hot表現にする
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y_train_cat = utils.to_categorical(y_train)
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y_test_cat = utils.to_categorical(y_test)
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y_realtest_cat = utils.to_categorical(y_realtest)
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labeled_M2.fit([x_train, y_train_cat],
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shuffle=True,
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epochs=epochs,
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batch_size=batch_size)
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# ラベルなしの時のモデル
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uy = Dense(intermediate_dim, activation='relu')(x)
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merge = Concatenate()([z, uy])
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h_decoded = decoder_h(merge)
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x_decoded_mean = decoder_mean(h_decoded)
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unlabeled_M2 = Model([x,uy],x_decoded_mean)
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unlabeled_M2.compile(optimizer='rmsprop', loss=vae_loss)
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labeled_M2.summary()
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unlabeled_M2.summary()
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```
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