Unverified Commit 3ff7e1ae authored by Jean-Marc Valin's avatar Jean-Marc Valin
Browse files

Adding the scripts used to train the RNN classifier

Sorry, no doc for now
parent 59f8e5e4
#!/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.models import load_model
from keras import backend as K
import sys
import numpy as np
def printVector(f, vector, name):
v = np.reshape(vector, (-1));
#print('static const float ', name, '[', len(v), '] = \n', file=f)
f.write('static const opus_int8 {}[{}] = {{\n '.format(name, len(v)))
for i in range(0, len(v)):
f.write('{}'.format(max(-128,min(127,int(round(128*v[i]))))))
if (i!=len(v)-1):
f.write(',')
else:
break;
if (i%8==7):
f.write("\n ")
else:
f.write(" ")
#print(v, file=f)
f.write('\n};\n\n')
return;
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)
#model = load_model(sys.argv[1], custom_objects={'binary_crossentrop2': binary_crossentrop2})
main_input = Input(shape=(None, 25), name='main_input')
x = Dense(32, activation='tanh')(main_input)
x = GRU(24, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
x = Dense(2, activation='sigmoid')(x)
model = Model(inputs=main_input, outputs=x)
model.load_weights(sys.argv[1])
weights = model.get_weights()
f = open(sys.argv[2], 'w')
f.write('/*This file is automatically generated from a Keras model*/\n\n')
f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "mlp.h"\n\n')
printVector(f, weights[0], 'layer0_weights')
printVector(f, weights[1], 'layer0_bias')
printVector(f, weights[2], 'layer1_weights')
printVector(f, weights[3], 'layer1_recur_weights')
printVector(f, weights[4], 'layer1_bias')
printVector(f, weights[5], 'layer2_weights')
printVector(f, weights[6], 'layer2_bias')
f.write('const DenseLayer layer0 = {\n layer0_bias,\n layer0_weights,\n 25, 32, 0\n};\n\n')
f.write('const GRULayer layer1 = {\n layer1_bias,\n layer1_weights,\n layer1_recur_weights,\n 32, 24\n};\n\n')
f.write('const DenseLayer layer2 = {\n layer2_bias,\n layer2_weights,\n 24, 2, 1\n};\n\n')
f.close()
#!/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")
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