| #!/usr/bin/python3 |
| |
| 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 CuDNNGRU |
| from keras.layers import SimpleRNN |
| from keras.layers import Dropout |
| from keras import losses |
| import h5py |
| from keras.optimizers import Adam |
| |
| from keras.constraints import Constraint |
| from keras import backend as K |
| import numpy as np |
| |
| import tensorflow as tf |
| from keras.backend.tensorflow_backend import set_session |
| config = tf.ConfigProto() |
| config.gpu_options.per_process_gpu_memory_fraction = 0.44 |
| set_session(tf.Session(config=config)) |
| |
| def binary_crossentrop2(y_true, y_pred): |
| return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_true, y_pred), axis=-1) |
| |
| def binary_accuracy2(y_true, y_pred): |
| return K.mean(K.cast(K.equal(y_true, K.round(y_pred)), 'float32') + K.cast(K.equal(y_true, 0.5), 'float32'), axis=-1) |
| |
| def quant_model(model): |
| weights = model.get_weights() |
| for k in range(len(weights)): |
| weights[k] = np.maximum(-128, np.minimum(127, np.round(128*weights[k])*0.0078125)) |
| model.set_weights(weights) |
| |
| class WeightClip(Constraint): |
| '''Clips the weights incident to each hidden unit to be inside a range |
| ''' |
| def __init__(self, c=2): |
| self.c = c |
| |
| def __call__(self, p): |
| return K.clip(p, -self.c, self.c) |
| |
| def get_config(self): |
| return {'name': self.__class__.__name__, |
| 'c': self.c} |
| |
| reg = 0.000001 |
| constraint = WeightClip(.998) |
| |
| print('Build model...') |
| |
| main_input = Input(shape=(None, 25), name='main_input') |
| x = Dense(32, activation='tanh', kernel_constraint=constraint, bias_constraint=constraint)(main_input) |
| #x = CuDNNGRU(24, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, bias_constraint=constraint)(x) |
| x = GRU(24, recurrent_activation='sigmoid', activation='tanh', return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, bias_constraint=constraint)(x) |
| x = Dense(2, activation='sigmoid', kernel_constraint=constraint, bias_constraint=constraint)(x) |
| model = Model(inputs=main_input, outputs=x) |
| |
| batch_size = 2048 |
| |
| print('Loading data...') |
| with h5py.File('features10b.h5', 'r') as hf: |
| all_data = hf['data'][:] |
| 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)) |
| |
| print("Marking ignores") |
| for s in y_train: |
| for e in s: |
| if (e[1] >= 1): |
| break |
| e[0] = 0.5 |
| |
| 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) |
| |
| model.load_weights('newweights10a1b_ep206.hdf5') |
| |
| #weights = model.get_weights() |
| #for k in range(len(weights)): |
| # weights[k] = np.round(128*weights[k])*0.0078125 |
| #model.set_weights(weights) |
| |
| # try using different optimizers and different optimizer configs |
| model.compile(loss=binary_crossentrop2, |
| optimizer=Adam(0.0001), |
| metrics=[binary_accuracy2]) |
| |
| print('Train...') |
| quant_model(model) |
| model.fit(x_train, y_train, |
| batch_size=batch_size, |
| epochs=10, validation_data=(x_train, y_train)) |
| model.save("newweights10a1c_ep10.hdf5") |
| |
| quant_model(model) |
| model.fit(x_train, y_train, |
| batch_size=batch_size, |
| epochs=50, initial_epoch=10) |
| model.save("newweights10a1c_ep50.hdf5") |
| |
| model.compile(loss=binary_crossentrop2, |
| optimizer=Adam(0.0001), |
| metrics=[binary_accuracy2]) |
| |
| quant_model(model) |
| model.fit(x_train, y_train, |
| batch_size=batch_size, |
| epochs=100, initial_epoch=50) |
| model.save("newweights10a1c_ep100.hdf5") |
| |
| quant_model(model) |
| model.fit(x_train, y_train, |
| batch_size=batch_size, |
| epochs=150, initial_epoch=100) |
| model.save("newweights10a1c_ep150.hdf5") |
| |
| quant_model(model) |
| model.fit(x_train, y_train, |
| batch_size=batch_size, |
| epochs=200, initial_epoch=150) |
| model.save("newweights10a1c_ep200.hdf5") |
| |
| quant_model(model) |
| model.fit(x_train, y_train, |
| batch_size=batch_size, |
| epochs=201, initial_epoch=200) |
| model.save("newweights10a1c_ep201.hdf5") |
| |
| quant_model(model) |
| model.fit(x_train, y_train, |
| batch_size=batch_size, |
| epochs=202, initial_epoch=201, validation_data=(x_train, y_train)) |
| model.save("newweights10a1c_ep202.hdf5") |
| |
| quant_model(model) |
| model.fit(x_train, y_train, |
| batch_size=batch_size, |
| epochs=203, initial_epoch=202, validation_data=(x_train, y_train)) |
| model.save("newweights10a1c_ep203.hdf5") |
| |
| quant_model(model) |
| model.fit(x_train, y_train, |
| batch_size=batch_size, |
| epochs=204, initial_epoch=203, validation_data=(x_train, y_train)) |
| model.save("newweights10a1c_ep204.hdf5") |
| |
| quant_model(model) |
| model.fit(x_train, y_train, |
| batch_size=batch_size, |
| epochs=205, initial_epoch=204, validation_data=(x_train, y_train)) |
| model.save("newweights10a1c_ep205.hdf5") |
| |
| quant_model(model) |
| model.fit(x_train, y_train, |
| batch_size=batch_size, |
| epochs=206, initial_epoch=205, validation_data=(x_train, y_train)) |
| model.save("newweights10a1c_ep206.hdf5") |
| |