usr/local/lib/python2.7/site-packages/keras/engine/training.pyc in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)ġ496 val_f=val_f, val_ins=val_ins, shuffle=shuffle,ġ500 def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None): Users/yjluo/WORK/pitchPerfect/vae/model2.py in ()ħ7 vae.fit(x=X, y=X, batch_size=batch_size, InvalidArgumentError Traceback (most recent call last) Vae.compile(optimizer='rmsprop', loss=vae_loss) Xent_loss = 10 * an_squared_error(x_, x_decoded_mean_) X_decoded_mean = Reshape()(x_decoded_mean) Padding='same', activation='relu')(upsamp) X_decoded_mean = Conv1D(1, kernel_size=num_conv, Padding='same', activation='relu')(de_conv_1) Padding='same', activation='relu')(decoder)ĭe_conv_2 = Conv1D(64, kernel_size=num_conv, Z = Lambda(sampling, output_shape=(latent_dim,))()ĭecoder_h = Dense(256, activation='relu')(z)ĭecoder = Dense(155, activation='relu')(decoder_h)ĭe_conv_1 = Conv1D(64, kernel_size=num_conv, Return(z_mean + K.exp(z_log_var/2) * epsilon) Hidden = Dense(intermediate_dim, activation='relu')(flatten)Įpsilon = K.random_normal(shape=(batch_size, latent_dim), ![]() ![]() Padding='same', activation='relu')(conv_2) Padding='same', strides=2, activation='relu')(conv_1)Ĭonv_3 = Conv1D(64, kernel_size=num_conv, From keras.layers import Input, Dense, Lambda, Flatten, Reshapeįrom keras.layers import Conv1D, UpSampling1D
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