def predict():
"""
An example of how to load a trained model and use it
to predict labels.
"""
# load the saved model
classifier = pickle.load(open(network_pickle))
# compile a predictor function
predict_model = theano.function(inputs=[classifier.x],
outputs=classifier.layers[-1].y_out)
# We can test it on some examples from test test
datasets = cl.load_crater_data_phaseII_wrapper(dataset, IMAGE_SIZE)
test_set_x, test_set_y = datasets[2]
test_set_x = test_set_x.get_value()
predicted_values = predict_model(test_set_x[0])
print("Predicted values for the first 10 examples in test set:")
print(predicted_values)
def load_data_shared():
global train_size, test_size, valid_size
training_data, test_data, validation_data = load_crater_data_phaseII_wrapper(
rotate=1)
train_size = training_data
test_size = test_data
valid_size = validation_data
def shared(data):
"""Place the data into shared variables. This allows Theano to copy
the data to the GPU, if one is available.
"""
shared_x = theano.shared(np.asarray(data[0],
dtype=theano.config.floatX),
borrow=True)
shared_y = theano.shared(np.asarray(data[1],
dtype=theano.config.floatX),
borrow=True)
return shared_x, T.cast(shared_y, "int32")
return [shared(training_data), shared(validation_data), shared(test_data)]
from network3 import *
from plotdatafitting import plotDataFit
import crater_loader
# uncomment if you do not want graph to show up.
IMAGE_SIZE = 28
EPOCHS = 20
MB_SIZE = 1
ETA = .00005
RUNS = 1
LAMBDA_LENGTH = 1
GEN = 3
PICKLE = "Pickle_Stash/GEN2-LReLU28x28-ntwk-e7-val0.9719-tst0.9705.pkl"
#training_data, validation_data, test_data = network3.load_data_shared()
# PHASE II -- Crater Data
training_data, validation_data, test_data = \
crater_loader.load_crater_data_phaseII_wrapper("non_rotated_28x28.pkl", IMAGE_SIZE)
if __name__ == "__main__":
net = cPickle.load(open(PICKLE, 'rb'))
os.system('rm Pickles/*.pkl')
net.SGD("GEN%s-LReLU28x28" % GEN,
training_data,
EPOCHS,
MB_SIZE,
ETA,
validation_data,
test_data,
lmbda=0.0001)
return epoch
def deploy_pickle(netwk):
best = netwk.ranker.best_epoch()
pickle_it((best.weights, best.biases), PICKLE_DIR)
os.system("rm -f *.qr")
os.system("touch %s/%s.qr" % (PICKLE_DIR, best.qr))
print "BEST EPOCH: %s" % best
# training_data, validation_data, test_data = network3.load_data_shared()
# PHASE II --- Crater Data
training_data, validation_data, test_data = \
crater_loader.load_crater_data_phaseII_wrapper("phase2-data.pkl", 28)
mini_batch_size = 10
def shallow(n=3, epochs=60):
nets = []
for j in range(n):
print "A shallow net with 100 hidden neurons"
net = Network([
FullyConnectedLayer(n_in=784, n_out=100),
SoftmaxLayer(n_in=100, n_out=10)
], mini_batch_size)
net.SGD(training_data, epochs, mini_batch_size, 0.1, validation_data,
test_data)
nets.append(net)
predict_model = theano.function(inputs=[classifier.x],
outputs=classifier.layers[-1].y_out)
# We can test it on some examples from test test
datasets = cl.load_crater_data_phaseII_wrapper(dataset, IMAGE_SIZE)
test_set_x, test_set_y = datasets[2]
test_set_x = test_set_x.get_value()
predicted_values = predict_model(test_set_x[0])
print("Predicted values for the first 10 examples in test set:")
print(predicted_values)
if __name__ == '__main__':
classifier = pickle.load(open(network_pickle))
datasets = cl.load_crater_data_phaseII_wrapper(dataset, IMAGE_SIZE)
test_set_x, test_set_y = datasets[0]
# # test_set_x = test_set_x.get_value()
# classifier.test_data = []
# for i in range(100):
# print classifier.test_mb_predictions(i)
i = T.lscalar()
pred = theano.function(
[i],
classifier.layers[-1].y_out,
givens={
classifier.x:
test_set_x[i * classifier.mini_batch_size:(i + 1) *
classifier.mini_batch_size]
})
import crater_loader
from plotdatafitting import plotDataFit
IMAGE_SIZE = 101
EPOCHS = 1
MB_SIZE = 1
ETA = .005
RUNS = 1
PICKLE = "Pickles/elu-network%sx%s" % (IMAGE_SIZE, IMAGE_SIZE)
#training_data, validation_data, test_data = network3.load_data_shared()
# PHASE II -- Crater Data
training_data, validation_data, test_data = \
crater_loader.load_crater_data_phaseII_wrapper("101x101.pkl", 101)
def leakyrelu():
net = None
for j in range(RUNS):
print "num %s, leaky relu, with regularization %s" % (j, 0.00001)
net = Network([
ConvPoolLayer(image_shape=(MB_SIZE, 1, IMAGE_SIZE, IMAGE_SIZE),
filter_shape=(5, 1, 15, 15),
poolsize=(3, 3),
activation_fn=LReLU),
ConvPoolLayer(image_shape=(MB_SIZE, 5, 29, 29),
filter_shape=(10, 5, 3, 3),
poolsize=(2, 2),
activation_fn=LReLU),