ref: 3ff7e1ae2d46ca325ee7cbbc14388ec138f4273f
parent: 59f8e5e4f8728391fde64eb56530f692fc6ce787
author: Jean-Marc Valin <jmvalin@jmvalin.ca>
date: Fri Nov 2 22:39:15 EDT 2018
Adding the scripts used to train the RNN classifier Sorry, no doc for now
--- /dev/null
+++ b/training/rnn_dump.py
@@ -1,0 +1,66 @@
+#!/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()
--- /dev/null
+++ b/training/rnn_train.py
@@ -1,0 +1,177 @@
+#!/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")+