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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1 @@ | ||
| {"C": 0, "N": 1, "5": 2, "l": 3, "O": 4, "s": 5, "7": 6, "r": 7, "(": 8, "[": 9, "=": 10, "P": 11, "o": 12, "]": 13, "#": 14, "6": 15, "3": 16, " ": 17, "I": 18, "c": 19, "4": 20, "+": 21, "-": 22, "n": 23, "H": 24, "8": 25, "\\": 26, "1": 27, "B": 28, "2": 29, ")": 30, "F": 31, "S": 32} |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,67 @@ | ||
| import matplotlib.pylab as plt | ||
| import numpy as np | ||
| import seaborn as sns; sns.set() | ||
| %matplotlib inline | ||
|
|
||
| import keras | ||
| from keras.models import Sequential, Model | ||
| from keras.layers import Dense | ||
| from keras.optimizers import Adam | ||
| import salty | ||
| from numpy import array | ||
| from numpy import argmax | ||
| from sklearn.preprocessing import LabelEncoder | ||
| from sklearn.preprocessing import OneHotEncoder | ||
| import numpy as np | ||
| from sklearn.model_selection import train_test_split | ||
| from random import shuffle | ||
|
|
||
| def | ||
|
|
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| def Encoder(x, latent_rep_size, smile_max_length, epsilon_std = 0.01): | ||
| h = Convolution1D(9, 9, activation = 'relu', name='conv_1')(x) | ||
| h = Convolution1D(9, 9, activation = 'relu', name='conv_2')(h) | ||
| h = Convolution1D(10, 11, activation = 'relu', name='conv_3')(h) | ||
| h = Flatten(name = 'flatten_1')(h) | ||
| h = Dense(435, activation = 'relu', name = 'dense_1')(h) | ||
|
|
||
| def sampling(args): | ||
| z_mean_, z_log_var_ = args | ||
| batch_size = K.shape(z_mean_)[0] | ||
| epsilon = K.random_normal(shape=(batch_size, latent_rep_size), | ||
| mean=0., stddev = epsilon_std) | ||
| return z_mean_ + K.exp(z_log_var_ / 2) * epsilon | ||
|
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||
| z_mean = Dense(latent_rep_size, name='z_mean', activation = 'linear')(h) | ||
| z_log_var = Dense(latent_rep_size, name='z_log_var', activation = 'linear')(h) | ||
|
|
||
| def vae_loss(x, x_decoded_mean): | ||
| x = K.flatten(x) | ||
| x_decoded_mean = K.flatten(x_decoded_mean) | ||
| xent_loss = smile_max_length * binary_crossentropy(x, x_decoded_mean) | ||
| kl_loss = - 0.5 * K.mean(1 + z_log_var - K.square(z_mean) - \ | ||
| K.exp(z_log_var), axis = -1) | ||
| return xent_loss + kl_loss | ||
|
|
||
| return (vae_loss, Lambda(sampling, output_shape=(latent_rep_size,), | ||
| name='lambda')([z_mean, z_log_var])) | ||
|
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| def Decoder(z, latent_rep_size, smile_max_length, charset_length): | ||
| h = Dense(latent_rep_size, name='latent_input', activation = 'relu')(z) | ||
| h = RepeatVector(smile_max_length, name='repeat_vector')(h) | ||
| h = GRU(501, return_sequences = True, name='gru_1')(h) | ||
| h = GRU(501, return_sequences = True, name='gru_2')(h) | ||
| h = GRU(501, return_sequences = True, name='gru_3')(h) | ||
| return TimeDistributed(Dense(charset_length, activation='softmax'), | ||
| name='decoded_mean')(h) | ||
|
|
||
| def sample(a, temperature=1.0): | ||
| # helper function to sample an index from a probability array | ||
| # a = np.log(a) / temperature | ||
| # a = np.exp(a) / np.sum(np.exp(a)) | ||
| # return np.argmax(np.random.multinomial(1, a, 1)) | ||
| # work around from https://github.com/llSourcell/How-to-Generate-Music-Demo/issues/4 | ||
| a = np.log(a) / temperature | ||
| dist = np.exp(a)/np.sum(np.exp(a)) | ||
| choices = range(len(a)) | ||
| return np.random.choice(choices, p=dist) |
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