force_no_dropout=True)
cnn.addPerceptronLayer(200, activation_func="tanh")
cnn.addPerceptronLayer(150, activation_func="tanh")
cnn.addPerceptronLayer(
10, activation_func="tanh", force_no_dropout=True
) # need 10 outputs as there are 10 classes in the data set
cnn.compileOutputFunctions()
cnn.setOptimizerParams(SGD={'LR': 1e-2, 'momentum': 0.9}, weight_decay=0)
print "training..."
for i in range(10000):
d, l = data.getbatch(batch_size)
loss, loss_instance, time_per_step = cnn.trainingStep(d, l, mode="SGD")
if i % 100 == 0:
r = cnn.getDropoutRates()
cnn.setDropoutRates([
0.0,
] * len(r))
valid_loss, valid_error, valid_predictions = cnn.get_error(
data.valid_d, data.valid_l)
print "update:", i, "; Validation loss:", valid_loss, "Validation error:", valid_error * 100., "%"
cnn.setDropoutRates(r)
cnn.setDropoutRates([
0.0,
] * len(r))
loss, error, test_predictions = cnn.get_error(data.test_d, data.test_l)
print "Test loss:", loss, "Test error:", error * 100., "%"
print "Done."
5,
pool_shape=2,
activation_func="tanh",
force_no_dropout=True)
cnn.addPerceptronLayer(200, activation_func="tanh")
cnn.addPerceptronLayer(150, activation_func="tanh")
cnn.addPerceptronLayer(
10,
activation_func="tanh",
force_no_dropout=True
) # need 10 outputs as there are 10 classes in the data set
cnn.compileOutputFunctions()
cnn.setOptimizerParams(SGD={'LR': 1e-2, 'momentum': 0.9}, weight_decay=0)
print "training..."
for i in range(10000):
d, l = data.getbatch(batch_size)
loss, loss_instance, time_per_step = cnn.trainingStep(d, l, mode="SGD")
if i % 100 == 0:
r = cnn.getDropoutRates()
cnn.setDropoutRates([0.0, ] * len(r))
valid_loss, valid_error, valid_predictions = cnn.get_error(data.valid_d, data.valid_l)
print "update:", i, "; Validation loss:", valid_loss, "Validation error:", valid_error * 100., "%"
cnn.setDropoutRates(r)
cnn.setDropoutRates([0.0, ] * len(r))
loss, error, test_predictions = cnn.get_error(data.test_d, data.test_l)
print "Test loss:", loss, "Test error:", error * 100., "%"
print "Done."
