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ann_train.py
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ann_train.py
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# ann_train.py
#
# Trains a neural net on a toy problem to generate a grid of zeros and
# ones. Training data is located in the tdata.txt file and is arranged
# as a 50x50 grid. For training, inputs are normalized on the interval
# [-1.0, 1.0]. The number of training pairs is 50 * 50 = 2500. There is
# a single output node.
import sys
import os
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras import optimizers
from keras.callbacks import Callback
def read_tfile(tfname):
"""read_tfile()
:param tfname: a text file "tdata.txt" containing a 50x50 grid of training data.
:return: (xtrain, ytrain), where xtrain is a numpy array with shape (2500, 2)
containing the x1,x2 coordinates of the training data, and ytrain is a numpy
array of shape (2500, 1) containing values 0.0 or 1.0 corresponding to the
x1,x2 coordinates in xtrain.
"""
sdat_ll = list()
with open(tfname) as fin:
for rlin in fin:
lin = rlin.strip()
if len(lin) < 20:
continue # line too small, so ignore it
sdat_ll.append(lin)
xdim = len(sdat_ll[0])
ydim = len(sdat_ll)
xincr = 2.0 / float(xdim - 1)
yincr = 2.0 / float(ydim - 1)
in_ll = list()
ot_ll = list()
y = -1.0
for iy, dlin in enumerate(sdat_ll):
x = -1.0
for ix, ch in enumerate(dlin):
if '.' == ch:
ot_ll.append((0.0,))
else:
ot_ll.append((1.0,))
in_ll.append((x + xincr * float(ix), y + yincr * float(iy)))
xtrain = np.array(in_ll)
ytrain = np.array(ot_ll)
return xtrain, ytrain
class EpochInstrospector(Callback):
"""Used to save weights after each epoch."""
def __init__(self, model, optimizer, progress_fname, weights_dir):
super(EpochInstrospector, self).__init__()
self.model = model
self.optimizer = optimizer
self.progress_fname = progress_fname
self.weights_dir = weights_dir
self.best_loss = 10000.0
def _save_weights(self, epoch, loss, acc):
wgts_np = self.model.get_weights()
fpname = os.path.join(self.weights_dir, '%05d' % epoch)
np.save(fpname, wgts_np)
with open(self.progress_fname, 'a') as fout:
fout.write("%d\t%.5f\t%5f\n" % (epoch, loss, acc))
def on_train_begin(self, logs=None):
self._save_weights(1, 0.5, 0.5)
def on_epoch_end(self, epoch, logs=None):
loss = logs['loss']
edx = epoch + 2
self._save_weights(edx, loss, logs['acc'])
class Toy01:
"""Sets up and trains the ANN using keras"""
def __init__(self, xtrain, ytrain, results_dir):
self.xtrain = xtrain
self.ytrain = ytrain
self.results_dir = results_dir
# ANN model
self.optimizer = optimizers.RMSprop(lr=0.005)
self.loss = 'mse'
self.metrics = 'accuracy'
self.model = Sequential()
self.model.add(Dense(output_dim=30, input_dim=2, init='uniform'))
self.model.add(Activation('relu'))
self.model.add(Dense(output_dim=30, init='uniform'))
self.model.add(Activation('relu'))
self.model.add(Dense(output_dim=20, init='uniform'))
self.model.add(Activation('relu'))
self.model.add(Dense(output_dim=1, init='uniform'))
self.model.add(Activation('sigmoid'))
# Compile the model
self.model.compile(
loss=self.loss,
optimizer=self.optimizer,
metrics=[self.metrics]
)
def process(self, nepochs):
progress_fname = os.path.join(self.results_dir, 'progress.txt')
model_fname = os.path.join(self.results_dir, 'ann_model.txt')
weights_dir = os.path.join(self.results_dir, 'weights')
if not os.path.exists(weights_dir):
os.mkdir(weights_dir, 0o755)
step = EpochInstrospector(self.model, self.optimizer, progress_fname, weights_dir)
fout = open(progress_fname, 'w')
fout.close()
with open(model_fname, 'w') as fout:
jmodel = self.model.to_json()
fout.write(jmodel)
self.model.fit(
self.xtrain,
self.ytrain,
batch_size=30,
nb_epoch=nepochs,
verbose=1,
callbacks=[step],
validation_split=0.0,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None
)
def run(tfname, results_dir, nepochs):
xtrain, ytrain = read_tfile(tfname)
toy = Toy01(xtrain, ytrain, results_dir)
toy.process(nepochs)
if __name__ == '__main__':
if len(sys.argv) < 4:
print("USE: ann_train <tfile> <resultsDir> <nepochs>")
sys.exit()
g_tfname = sys.argv[1]
g_results_dir = sys.argv[2]
g_nepochs = int(sys.argv[3])
run(g_tfname, g_results_dir, g_nepochs)