You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
68 lines
2.1 KiB
68 lines
2.1 KiB
4 months ago
|
#!/usr/bin/python
|
||
|
|
|
||
|
|
from __future__ import print_function
|
||
|
|
|
||
|
|
from keras.models import Sequential
|
||
|
|
from keras.models import Model
|
||
|
|
from keras.layers import Input
|
||
|
|
from keras.layers import Dense
|
||
|
|
from keras.layers import LSTM
|
||
|
|
from keras.layers import GRU
|
||
|
|
from keras.layers import SimpleRNN
|
||
|
|
from keras.layers import Dropout
|
||
|
|
from keras import losses
|
||
|
|
import h5py
|
||
|
|
|
||
|
|
from keras import backend as K
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
def binary_crossentrop2(y_true, y_pred):
|
||
|
|
return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
|
||
|
|
|
||
|
|
print('Build model...')
|
||
|
|
#model = Sequential()
|
||
|
|
#model.add(Dense(16, activation='tanh', input_shape=(None, 25)))
|
||
|
|
#model.add(GRU(12, dropout=0.0, recurrent_dropout=0.0, activation='tanh', recurrent_activation='sigmoid', return_sequences=True))
|
||
|
|
#model.add(Dense(2, activation='sigmoid'))
|
||
|
|
|
||
|
|
main_input = Input(shape=(None, 25), name='main_input')
|
||
|
|
x = Dense(16, activation='tanh')(main_input)
|
||
|
|
x = GRU(12, dropout=0.1, recurrent_dropout=0.1, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
|
||
|
|
x = Dense(2, activation='sigmoid')(x)
|
||
|
|
model = Model(inputs=main_input, outputs=x)
|
||
|
|
|
||
|
|
batch_size = 64
|
||
|
|
|
||
|
|
print('Loading data...')
|
||
|
|
with h5py.File('features.h5', 'r') as hf:
|
||
|
|
all_data = hf['features'][:]
|
||
|
|
print('done.')
|
||
|
|
|
||
|
|
window_size = 1500
|
||
|
|
|
||
|
|
nb_sequences = len(all_data)/window_size
|
||
|
|
print(nb_sequences, ' sequences')
|
||
|
|
x_train = all_data[:nb_sequences*window_size, :-2]
|
||
|
|
x_train = np.reshape(x_train, (nb_sequences, window_size, 25))
|
||
|
|
|
||
|
|
y_train = np.copy(all_data[:nb_sequences*window_size, -2:])
|
||
|
|
y_train = np.reshape(y_train, (nb_sequences, window_size, 2))
|
||
|
|
|
||
|
|
all_data = 0;
|
||
|
|
x_train = x_train.astype('float32')
|
||
|
|
y_train = y_train.astype('float32')
|
||
|
|
|
||
|
|
print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape)
|
||
|
|
|
||
|
|
# try using different optimizers and different optimizer configs
|
||
|
|
model.compile(loss=binary_crossentrop2,
|
||
|
|
optimizer='adam',
|
||
|
|
metrics=['binary_accuracy'])
|
||
|
|
|
||
|
|
print('Train...')
|
||
|
|
model.fit(x_train, y_train,
|
||
|
|
batch_size=batch_size,
|
||
|
|
epochs=200,
|
||
|
|
validation_data=(x_train, y_train))
|
||
|
|
model.save("newweights.hdf5")
|