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Adding the scripts used to train the RNN classifier
Sorry, no doc for now
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Jean-Marc Valin
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2 changed files with 243 additions and 0 deletions
66
training/rnn_dump.py
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66
training/rnn_dump.py
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#!/usr/bin/python
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from __future__ import print_function
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from keras.models import Sequential
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from keras.models import Model
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from keras.layers import Input
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from keras.layers import Dense
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from keras.layers import LSTM
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from keras.layers import GRU
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from keras.models import load_model
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from keras import backend as K
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import sys
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import numpy as np
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def printVector(f, vector, name):
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v = np.reshape(vector, (-1));
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#print('static const float ', name, '[', len(v), '] = \n', file=f)
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f.write('static const opus_int8 {}[{}] = {{\n '.format(name, len(v)))
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for i in range(0, len(v)):
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f.write('{}'.format(max(-128,min(127,int(round(128*v[i]))))))
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if (i!=len(v)-1):
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f.write(',')
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else:
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break;
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if (i%8==7):
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f.write("\n ")
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else:
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f.write(" ")
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#print(v, file=f)
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f.write('\n};\n\n')
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return;
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def binary_crossentrop2(y_true, y_pred):
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return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
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#model = load_model(sys.argv[1], custom_objects={'binary_crossentrop2': binary_crossentrop2})
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main_input = Input(shape=(None, 25), name='main_input')
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x = Dense(32, activation='tanh')(main_input)
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x = GRU(24, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
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x = Dense(2, activation='sigmoid')(x)
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model = Model(inputs=main_input, outputs=x)
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model.load_weights(sys.argv[1])
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weights = model.get_weights()
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f = open(sys.argv[2], 'w')
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f.write('/*This file is automatically generated from a Keras model*/\n\n')
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f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "mlp.h"\n\n')
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printVector(f, weights[0], 'layer0_weights')
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printVector(f, weights[1], 'layer0_bias')
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printVector(f, weights[2], 'layer1_weights')
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printVector(f, weights[3], 'layer1_recur_weights')
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printVector(f, weights[4], 'layer1_bias')
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printVector(f, weights[5], 'layer2_weights')
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printVector(f, weights[6], 'layer2_bias')
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f.write('const DenseLayer layer0 = {\n layer0_bias,\n layer0_weights,\n 25, 32, 0\n};\n\n')
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f.write('const GRULayer layer1 = {\n layer1_bias,\n layer1_weights,\n layer1_recur_weights,\n 32, 24\n};\n\n')
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f.write('const DenseLayer layer2 = {\n layer2_bias,\n layer2_weights,\n 24, 2, 1\n};\n\n')
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f.close()
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177
training/rnn_train.py
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training/rnn_train.py
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#!/usr/bin/python3
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from __future__ import print_function
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from keras.models import Sequential
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from keras.models import Model
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from keras.layers import Input
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from keras.layers import Dense
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from keras.layers import LSTM
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from keras.layers import GRU
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from keras.layers import CuDNNGRU
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from keras.layers import SimpleRNN
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from keras.layers import Dropout
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from keras import losses
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import h5py
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from keras.optimizers import Adam
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from keras.constraints import Constraint
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from keras import backend as K
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import numpy as np
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import tensorflow as tf
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from keras.backend.tensorflow_backend import set_session
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config = tf.ConfigProto()
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config.gpu_options.per_process_gpu_memory_fraction = 0.44
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set_session(tf.Session(config=config))
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def binary_crossentrop2(y_true, y_pred):
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return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_true, y_pred), axis=-1)
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def binary_accuracy2(y_true, y_pred):
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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)
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def quant_model(model):
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weights = model.get_weights()
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for k in range(len(weights)):
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weights[k] = np.maximum(-128, np.minimum(127, np.round(128*weights[k])*0.0078125))
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model.set_weights(weights)
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class WeightClip(Constraint):
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'''Clips the weights incident to each hidden unit to be inside a range
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'''
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def __init__(self, c=2):
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self.c = c
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def __call__(self, p):
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return K.clip(p, -self.c, self.c)
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def get_config(self):
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return {'name': self.__class__.__name__,
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'c': self.c}
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reg = 0.000001
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constraint = WeightClip(.998)
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print('Build model...')
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main_input = Input(shape=(None, 25), name='main_input')
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x = Dense(32, activation='tanh', kernel_constraint=constraint, bias_constraint=constraint)(main_input)
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#x = CuDNNGRU(24, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, bias_constraint=constraint)(x)
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x = GRU(24, recurrent_activation='sigmoid', activation='tanh', return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, bias_constraint=constraint)(x)
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x = Dense(2, activation='sigmoid', kernel_constraint=constraint, bias_constraint=constraint)(x)
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model = Model(inputs=main_input, outputs=x)
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batch_size = 2048
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print('Loading data...')
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with h5py.File('features10b.h5', 'r') as hf:
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all_data = hf['data'][:]
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print('done.')
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window_size = 1500
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nb_sequences = len(all_data)//window_size
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print(nb_sequences, ' sequences')
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x_train = all_data[:nb_sequences*window_size, :-2]
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x_train = np.reshape(x_train, (nb_sequences, window_size, 25))
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y_train = np.copy(all_data[:nb_sequences*window_size, -2:])
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y_train = np.reshape(y_train, (nb_sequences, window_size, 2))
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print("Marking ignores")
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for s in y_train:
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for e in s:
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if (e[1] >= 1):
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break
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e[0] = 0.5
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all_data = 0;
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x_train = x_train.astype('float32')
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y_train = y_train.astype('float32')
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print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape)
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model.load_weights('newweights10a1b_ep206.hdf5')
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#weights = model.get_weights()
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#for k in range(len(weights)):
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# weights[k] = np.round(128*weights[k])*0.0078125
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#model.set_weights(weights)
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# try using different optimizers and different optimizer configs
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model.compile(loss=binary_crossentrop2,
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optimizer=Adam(0.0001),
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metrics=[binary_accuracy2])
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print('Train...')
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quant_model(model)
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=10, validation_data=(x_train, y_train))
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model.save("newweights10a1c_ep10.hdf5")
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quant_model(model)
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=50, initial_epoch=10)
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model.save("newweights10a1c_ep50.hdf5")
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model.compile(loss=binary_crossentrop2,
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optimizer=Adam(0.0001),
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metrics=[binary_accuracy2])
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quant_model(model)
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=100, initial_epoch=50)
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model.save("newweights10a1c_ep100.hdf5")
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quant_model(model)
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=150, initial_epoch=100)
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model.save("newweights10a1c_ep150.hdf5")
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quant_model(model)
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=200, initial_epoch=150)
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model.save("newweights10a1c_ep200.hdf5")
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quant_model(model)
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=201, initial_epoch=200)
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model.save("newweights10a1c_ep201.hdf5")
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quant_model(model)
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=202, initial_epoch=201, validation_data=(x_train, y_train))
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model.save("newweights10a1c_ep202.hdf5")
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quant_model(model)
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=203, initial_epoch=202, validation_data=(x_train, y_train))
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model.save("newweights10a1c_ep203.hdf5")
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quant_model(model)
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=204, initial_epoch=203, validation_data=(x_train, y_train))
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model.save("newweights10a1c_ep204.hdf5")
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quant_model(model)
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=205, initial_epoch=204, validation_data=(x_train, y_train))
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model.save("newweights10a1c_ep205.hdf5")
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quant_model(model)
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=206, initial_epoch=205, validation_data=(x_train, y_train))
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model.save("newweights10a1c_ep206.hdf5")
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