#!/usr/bin/env python
import numpy as np
import keras
from keras.models import Sequential, Dense, SimpleRNN
from keras.preprocessing import sequence
from sklearn.preprocessing import MinMaxScaler
from keras.utils import np_utils
import itertools, argparse
def network_simple_rnn(data_in, out_dim, optim_type, b_size, save_file, num_classes, epochs, default_val):
X_train = data_in[0]
dummy_y = data_in[1]
X_test = data_in[2]
dummy_y_test = data_in[3]
model = Sequential()
model.add(SimpleRNN(out_dim, input_shape = (X_train.shape[1], X_train.shape[2]), return_sequences = False))
model.add(Dense(num_classes, activation='sigmoid'))
optim_type = ["rmsprop", "adam", "sgd"]
s_in = save_file
for optim_val in optim_type:
if optim_val == "sgd" and default_val == False:
lr_ = [0.001, 0.01, 0.05]
momentum_in = [0., 0.8, 0.9, 0.99]
decay_in = [0., 0.01, 0.1, 0.5]
nest_in = [True, False]
paras_in = itertools.product(lr_, momentum_in, decay_in, nest_in)
for l_in, m_in, d_in, n_in in paras_in:
save_file = s_in
optim_use = keras.optimizers.sgd(lr = l_in, momentum = m_in, decay = d_in, nesterov = n_in)
model.compile(loss='categorical_crossentropy', optimizer = optim_use, metrics = ['accuracy'])
hist = model.fit(X_train, dummy_y, validation_data=(X_test, dummy_y_test), nb_epoch = epochs, batch_size = b_size)
scores = model.evaluate(X_train, dummy_y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
h1 = hist.history
acc_ = np.asarray(h1['acc']) #ndarray
loss_ = np.asarray(h1['loss']) #ndarray
val_loss_ = np.asarray(h1['val_loss'])
val_acc_ = np.asarray(h1['val_acc'])
acc_and_loss = np.column_stack((acc_, loss_, val_acc_, val_loss_))
save_file = save_file + str(l_in) + str(m_in) + str(d_in) + str(n_in) + str(epochs) + ".txt"
print 'saving file'
#Write the scores to a file
with open(save_file, 'w') as f:
np.savetxt(save_file, acc_and_loss, delimiter=" ")
print 'saved file', save_file
else:
model.compile(loss='categorical_crossentropy', optimizer = optim_val, metrics = ['accuracy'])
hist = model.fit(X_train, dummy_y, validation_data=(X_test, dummy_y_test), nb_epoch = epochs, batch_size = b_size)
scores = model.evaluate(X_train, dummy_y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
save_file = s_in
h1 = hist.history
acc_ = np.asarray(h1['acc']) #ndarray
loss_ = np.asarray(h1['loss']) #ndarray
val_loss_ = np.asarray(h1['val_loss'])
val_acc_ = np.asarray(h1['val_acc'])
acc_and_loss = np.column_stack((acc_, loss_, val_acc_, val_loss_))
save_file = save_file + str(optim_type) + str(epochs) + ".txt"
print 'saving file'
with open(save_file, 'w') as f:
np.savetxt(save_file, acc_and_loss, delimiter=" ")
print 'saved file', save_file
if __name__ == '__main__':
#This section reads in command line arguments from a separate file
parser = argparse.ArgumentParser()
parser.add_argument('--train_file')
parser.add_argument('--test_file')
parser.add_argument('--out_dim')
parser.add_argument('--optim_type')
parser.add_argument('--batch_size') #vector of same length as num_layers
parser.add_argument('--save_file') #initial
parser.add_argument('--num_classes')
parser.add_argument('--epochs') #48
parser.add_argument('--default_val') #48
args = parser.parse_args()
train_file = str(args.train_file)
test_file = str(args.test_file)
out_dim = int(args.out_dim)
optim_type = str(args.optim_type)
b_size = int(args.batch_size)
save_file = str(args.save_file)
num_classes = int(args.num_classes)
epochs = int(args.epochs)
default_val = bool(args.default_val)
data_in = read_data(train_file, test_file)
network_simple_rnn(data_in, out_dim, optim_type, b_size, save_file, num_classes, epochs, default_val)
#!/usr/bin/env python
import numpy as np
import keras
from keras.models import Sequential, Dense, SimpleRNN
from keras.preprocessing import sequence
from sklearn.preprocessing import MinMaxScaler
from keras.utils import np_utils
import itertools, argparse
def network_simple_rnn(data_in, out_dim, optim_type, b_size, save_file, num_classes, epochs, default_val):
X_train = data_in[0]
dummy_y = data_in[1]
X_test = data_in[2]
dummy_y_test = data_in[3]
model = Sequential()
model.add(SimpleRNN(out_dim, input_shape = (X_train.shape[1], X_train.shape[2]), return_sequences = False))
model.add(Dense(num_classes, activation='sigmoid'))
optim_type = ["rmsprop", "adam", "sgd"]
s_in = save_file
for optim_val in optim_type:
if optim_val == "sgd" and default_val == False:
lr_ = [0.001, 0.01, 0.05]
momentum_in = [0., 0.8, 0.9, 0.99]
decay_in = [0., 0.01, 0.1, 0.5]
nest_in = [True, False]
paras_in = itertools.product(lr_, momentum_in, decay_in, nest_in)
for l_in, m_in, d_in, n_in in paras_in:
save_file = s_in
optim_use = keras.optimizers.sgd(lr = l_in, momentum = m_in, decay = d_in, nesterov = n_in)
model.compile(loss='categorical_crossentropy', optimizer = optim_use, metrics = ['accuracy'])
hist = model.fit(X_train, dummy_y, validation_data=(X_test, dummy_y_test), nb_epoch = epochs, batch_size = b_size)
scores = model.evaluate(X_train, dummy_y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
h1 = hist.history
acc_ = np.asarray(h1['acc']) #ndarray
loss_ = np.asarray(h1['loss']) #ndarray
val_loss_ = np.asarray(h1['val_loss'])
val_acc_ = np.asarray(h1['val_acc'])
acc_and_loss = np.column_stack((acc_, loss_, val_acc_, val_loss_))
save_file = save_file + str(l_in) + str(m_in) + str(d_in) + str(n_in) + str(epochs) + ".txt"
print 'saving file'
#Write the scores to a file
with open(save_file, 'w') as f:
np.savetxt(save_file, acc_and_loss, delimiter=" ")
print 'saved file', save_file
else:
model.compile(loss='categorical_crossentropy', optimizer = optim_val, metrics = ['accuracy'])
hist = model.fit(X_train, dummy_y, validation_data=(X_test, dummy_y_test), nb_epoch = epochs, batch_size = b_size)
scores = model.evaluate(X_train, dummy_y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
save_file = s_in
h1 = hist.history
acc_ = np.asarray(h1['acc']) #ndarray
loss_ = np.asarray(h1['loss']) #ndarray
val_loss_ = np.asarray(h1['val_loss'])
val_acc_ = np.asarray(h1['val_acc'])
acc_and_loss = np.column_stack((acc_, loss_, val_acc_, val_loss_))
save_file = save_file + str(optim_type) + str(epochs) + ".txt"
print 'saving file'
with open(save_file, 'w') as f:
np.savetxt(save_file, acc_and_loss, delimiter=" ")
print 'saved file', save_file
if __name__ == '__main__':
#This section reads in command line arguments from a separate file
parser = argparse.ArgumentParser()
parser.add_argument('--train_file')
parser.add_argument('--test_file')
parser.add_argument('--out_dim')
parser.add_argument('--optim_type')
parser.add_argument('--batch_size') #vector of same length as num_layers
parser.add_argument('--save_file') #initial
parser.add_argument('--num_classes')
parser.add_argument('--epochs') #48
parser.add_argument('--default_val') #48
args = parser.parse_args()
train_file = str(args.train_file)
test_file = str(args.test_file)
out_dim = int(args.out_dim)
optim_type = str(args.optim_type)
b_size = int(args.batch_size)
save_file = str(args.save_file)
num_classes = int(args.num_classes)
epochs = int(args.epochs)
default_val = bool(args.default_val)
data_in = read_data(train_file, test_file)
network_simple_rnn(data_in, out_dim, optim_type, b_size, save_file, num_classes, epochs, default_val)
#!/usr/bin/env python
import numpy as np
import keras
from keras.models import Sequential, Dense, SimpleRNN
from keras.preprocessing import sequence
from sklearn.preprocessing import MinMaxScaler
from keras.utils import np_utils
import itertools, argparse
def network_simple_rnn(data_in, out_dim, optim_type, b_size, save_file, num_classes, epochs, default_val):
X_train = data_in[0]
dummy_y = data_in[1]
X_test = data_in[2]
dummy_y_test = data_in[3]
model = Sequential()
model.add(SimpleRNN(out_dim, input_shape = (X_train.shape[1], X_train.shape[2]), return_sequences = False))
model.add(Dense(num_classes, activation='sigmoid'))
optim_type = ["rmsprop", "adam", "sgd"]
s_in = save_file
for optim_val in optim_type:
if optim_val == "sgd" and default_val == False:
lr_ = [0.001, 0.01, 0.05]
momentum_in = [0., 0.8, 0.9, 0.99]
decay_in = [0., 0.01, 0.1, 0.5]
nest_in = [True, False]
paras_in = itertools.product(lr_, momentum_in, decay_in, nest_in)
for l_in, m_in, d_in, n_in in paras_in:
save_file = s_in
optim_use = keras.optimizers.sgd(lr = l_in, momentum = m_in, decay = d_in, nesterov = n_in)
model.compile(loss='categorical_crossentropy', optimizer = optim_use, metrics = ['accuracy'])
hist = model.fit(X_train, dummy_y, validation_data=(X_test, dummy_y_test), nb_epoch = epochs, batch_size = b_size)
scores = model.evaluate(X_train, dummy_y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
h1 = hist.history
acc_ = np.asarray(h1['acc']) #ndarray
loss_ = np.asarray(h1['loss']) #ndarray
val_loss_ = np.asarray(h1['val_loss'])
val_acc_ = np.asarray(h1['val_acc'])
acc_and_loss = np.column_stack((acc_, loss_, val_acc_, val_loss_))
save_file = save_file + str(l_in) + str(m_in) + str(d_in) + str(n_in) + str(epochs) + ".txt"
print 'saving file'
#Write the scores to a file
with open(save_file, 'w') as f:
np.savetxt(save_file, acc_and_loss, delimiter=" ")
print 'saved file', save_file
else:
model.compile(loss='categorical_crossentropy', optimizer = optim_val, metrics = ['accuracy'])
hist = model.fit(X_train, dummy_y, validation_data=(X_test, dummy_y_test), nb_epoch = epochs, batch_size = b_size)
scores = model.evaluate(X_train, dummy_y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
save_file = s_in
h1 = hist.history
acc_ = np.asarray(h1['acc']) #ndarray
loss_ = np.asarray(h1['loss']) #ndarray
val_loss_ = np.asarray(h1['val_loss'])
val_acc_ = np.asarray(h1['val_acc'])
acc_and_loss = np.column_stack((acc_, loss_, val_acc_, val_loss_))
save_file = save_file + str(optim_type) + str(epochs) + ".txt"
print 'saving file'
with open(save_file, 'w') as f:
np.savetxt(save_file, acc_and_loss, delimiter=" ")
print 'saved file', save_file
if __name__ == '__main__':
#This section reads in command line arguments from a separate file
parser = argparse.ArgumentParser()
parser.add_argument('--train_file')
parser.add_argument('--test_file')
parser.add_argument('--out_dim')
parser.add_argument('--optim_type')
parser.add_argument('--batch_size')
parser.add_argument('--save_file')
parser.add_argument('--num_classes')
parser.add_argument('--epochs')
parser.add_argument('--default_val')
args = parser.parse_args()
train_file = str(args.train_file)
test_file = str(args.test_file)
out_dim = int(args.out_dim)
optim_type = str(args.optim_type)
b_size = int(args.batch_size)
save_file = str(args.save_file)
num_classes = int(args.num_classes)
epochs = int(args.epochs)
default_val = bool(args.default_val)
data_in = read_data(train_file, test_file)
network_simple_rnn(data_in, out_dim, optim_type, b_size, save_file, num_classes, epochs, default_val)
Keras: multiclass classification with Recurrent Neural Network
Dataset: Labelled epidemic data consisting of number of infectious individuals per unit time.
Challenge: Use supervised classification via a recurrent neural network to classify each epidemic as belonging to one of eight classes.
My problem: I have working code, but I have a feeling it's not the best way to approach the problem. In particular, I have assumed that hyperparameters like number of units per layer, learning rate, batch size, etc. come from a discrete set, and I run a different neural network for each setting. There must be a standard (better) way of doing this?
Relevant section of working code: (Disclaimer: huge debt of gratitude to http://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/)
#!/usr/bin/env python
import numpy as np
import keras
from keras.models import Sequential, Dense, SimpleRNN
from keras.preprocessing import sequence
from sklearn.preprocessing import MinMaxScaler
from keras.utils import np_utils
import itertools, argparse
def network_simple_rnn(data_in, out_dim, optim_type, b_size, save_file, num_classes, epochs, default_val):
X_train = data_in[0]
dummy_y = data_in[1]
X_test = data_in[2]
dummy_y_test = data_in[3]
model = Sequential()
model.add(SimpleRNN(out_dim, input_shape = (X_train.shape[1], X_train.shape[2]), return_sequences = False))
model.add(Dense(num_classes, activation='sigmoid'))
optim_type = ["rmsprop", "adam", "sgd"]
s_in = save_file
for optim_val in optim_type:
if optim_val == "sgd" and default_val == False:
lr_ = [0.001, 0.01, 0.05]
momentum_in = [0., 0.8, 0.9, 0.99]
decay_in = [0., 0.01, 0.1, 0.5]
nest_in = [True, False]
paras_in = itertools.product(lr_, momentum_in, decay_in, nest_in)
for l_in, m_in, d_in, n_in in paras_in:
save_file = s_in
optim_use = keras.optimizers.sgd(lr = l_in, momentum = m_in, decay = d_in, nesterov = n_in)
model.compile(loss='categorical_crossentropy', optimizer = optim_use, metrics = ['accuracy'])
hist = model.fit(X_train, dummy_y, validation_data=(X_test, dummy_y_test), nb_epoch = epochs, batch_size = b_size)
scores = model.evaluate(X_train, dummy_y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
h1 = hist.history
acc_ = np.asarray(h1['acc']) #ndarray
loss_ = np.asarray(h1['loss']) #ndarray
val_loss_ = np.asarray(h1['val_loss'])
val_acc_ = np.asarray(h1['val_acc'])
acc_and_loss = np.column_stack((acc_, loss_, val_acc_, val_loss_))
save_file = save_file + str(l_in) + str(m_in) + str(d_in) + str(n_in) + str(epochs) + ".txt"
print 'saving file'
#Write the scores to a file
with open(save_file, 'w') as f:
np.savetxt(save_file, acc_and_loss, delimiter=" ")
print 'saved file', save_file
else:
model.compile(loss='categorical_crossentropy', optimizer = optim_val, metrics = ['accuracy'])
hist = model.fit(X_train, dummy_y, validation_data=(X_test, dummy_y_test), nb_epoch = epochs, batch_size = b_size)
scores = model.evaluate(X_train, dummy_y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
save_file = s_in
h1 = hist.history
acc_ = np.asarray(h1['acc']) #ndarray
loss_ = np.asarray(h1['loss']) #ndarray
val_loss_ = np.asarray(h1['val_loss'])
val_acc_ = np.asarray(h1['val_acc'])
acc_and_loss = np.column_stack((acc_, loss_, val_acc_, val_loss_))
save_file = save_file + str(optim_type) + str(epochs) + ".txt"
print 'saving file'
with open(save_file, 'w') as f:
np.savetxt(save_file, acc_and_loss, delimiter=" ")
print 'saved file', save_file
if __name__ == '__main__':
#This section reads in command line arguments from a separate file
parser = argparse.ArgumentParser()
parser.add_argument('--train_file')
parser.add_argument('--test_file')
parser.add_argument('--out_dim')
parser.add_argument('--optim_type')
parser.add_argument('--batch_size') #vector of same length as num_layers
parser.add_argument('--save_file') #initial
parser.add_argument('--num_classes')
parser.add_argument('--epochs') #48
parser.add_argument('--default_val') #48
args = parser.parse_args()
train_file = str(args.train_file)
test_file = str(args.test_file)
out_dim = int(args.out_dim)
optim_type = str(args.optim_type)
b_size = int(args.batch_size)
save_file = str(args.save_file)
num_classes = int(args.num_classes)
epochs = int(args.epochs)
default_val = bool(args.default_val)
data_in = read_data(train_file, test_file)
network_simple_rnn(data_in, out_dim, optim_type, b_size, save_file, num_classes, epochs, default_val)
lang-py