def run_mlp():
# # define the model layers
# layer1 = Dense(input_size=784, output_size=1000, activation='rectifier')
# layer2 = Dense(inputs_hook=(1000, layer1.get_outputs()), output_size=1000, activation='rectifier')
# classlayer3 = SoftmaxLayer(inputs_hook=(1000, layer2.get_outputs()), output_size=10, out_as_probs=False)
# # add the layers to the prototype
# mlp = Prototype(layers=[layer1, layer2, classlayer3])
# test the new way to automatically fill in inputs_hook for models
mlp = Prototype()
mlp.add(Dense(input_size=784, output_size=1000, activation='rectifier', noise='dropout'))
mlp.add(Dense(output_size=1500, activation='tanh', noise='dropout'))
mlp.add(SoftmaxLayer(output_size=10))
mnist = MNIST()
optimizer = AdaDelta(model=mlp, dataset=mnist, epochs=10)
optimizer.train()
test_data, test_labels = mnist.test_inputs, mnist.test_targets
test_data = test_data[:25]
test_labels = test_labels[:25]
# use the run function!
yhat = mlp.run(test_data)
print('-------')
print('Prediction: %s' % str(yhat))
print('Actual: %s' % str(test_labels.astype('int32')))
def run_mlp():
# # define the model layers
# layer1 = BasicLayer(input_size=784, output_size=1000, activation='rectifier')
# layer2 = BasicLayer(inputs_hook=(1000, layer1.get_outputs()), output_size=1000, activation='rectifier')
# classlayer3 = SoftmaxLayer(inputs_hook=(1000, layer2.get_outputs()), output_size=10, out_as_probs=False)
# # add the layers to the prototype
# mlp = Prototype(layers=[layer1, layer2, classlayer3])
# test the new way to automatically fill in inputs_hook for models
mlp = Prototype()
mlp.add(BasicLayer(input_size=784, output_size=1000, activation="rectifier", noise="dropout"))
mlp.add(BasicLayer(output_size=1500, activation="tanh", noise="dropout"))
mlp.add(SoftmaxLayer(output_size=10))
mnist = MNIST()
optimizer = AdaDelta(model=mlp, dataset=mnist, n_epoch=10)
optimizer.train()
test_data, test_labels = mnist.getSubset(subset=TEST)
test_data = test_data[:25].eval()
test_labels = test_labels[:25].eval()
# use the run function!
yhat = mlp.run(test_data)
print("-------")
print("Prediction: %s" % str(yhat))
print("Actual: %s" % str(test_labels.astype("int32")))
def create_mlp():
# define the model layers
relu_layer1 = BasicLayer(input_size=784, output_size=1000, activation='rectifier')
relu_layer2 = BasicLayer(inputs_hook=(1000, relu_layer1.get_outputs()), output_size=1000, activation='rectifier')
class_layer3 = SoftmaxLayer(inputs_hook=(1000, relu_layer2.get_outputs()), output_size=10, out_as_probs=False)
# add the layers as a Prototype
mlp = Prototype(layers=[relu_layer1, relu_layer2, class_layer3])
mnist = MNIST()
optimizer = AdaDelta(model=mlp, dataset=mnist, n_epoch=20)
optimizer.train()
test_data, test_labels = mnist.getSubset(TEST)
test_data = test_data[:25].eval()
test_labels = test_labels[:25].eval()
# use the run function!
preds = mlp.run(test_data)
log.info('-------')
log.info("predicted: %s",str(preds))
log.info("actual: %s",str(test_labels.astype('int32')))
outputs=10,
out_as_probs=True)
# create the mlp from the two layers
mlp = Prototype(layers=[layer1, layer1_act, layer2])
# define the loss function
loss = Neg_LL(inputs=mlp.get_outputs(),
targets=vector("y", dtype="int64"),
one_hot=False)
# make an optimizer to train it (AdaDelta is a good default)
# optimizer = AdaDelta(model=mlp, dataset=mnist, n_epoch=20)
optimizer = AdaDelta(dataset=mnist, loss=loss, epochs=20)
# perform training!
# optimizer.train()
mlp.train(optimizer)
# test it on some images!
test_data, test_labels = mnist.test_inputs, mnist.test_targets
test_data = test_data[:25]
test_labels = test_labels[:25]
# use the run function!
preds = mlp.run(test_data)[0]
print('-------')
print(argmax(preds, axis=1))
print(test_labels.astype('int32'))
print()
print()
del mnist
del mlp
del optimizer
# define the loss function
loss = Neg_LL(inputs=mlp.get_outputs(), targets=vector("y", dtype="int64"), one_hot=False)
#plot the loss
if BOKEH_AVAILABLE:
plot = Plot("mlp_mnist", monitor_channels=Monitor("loss", loss.get_loss()), open_browser=True)
else:
plot = None
# make an optimizer to train it (AdaDelta is a good default)
# optimizer = AdaDelta(model=mlp, dataset=mnist, n_epoch=20)
optimizer = AdaDelta(dataset=mnist, loss=loss, epochs=20)
# perform training!
# optimizer.train()
mlp.train(optimizer, plot=plot)
# test it on some images!
test_data, test_labels = mnist.test_inputs, mnist.test_targets
test_data = test_data[:25]
test_labels = test_labels[:25]
# use the run function!
preds = mlp.run(test_data)
print('-------')
print(argmax(preds, axis=1))
print(test_labels.astype('int32'))
print()
print()
del mnist
del mlp
del optimizer
