layer2 = Softmax(inputs=((None, 1000), layer1_act.get_outputs()),
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
layer1_act = Activation(inputs=((None, 1000), layer1.get_outputs()), activation='relu')
# create the softmax classifier
layer2 = Softmax(inputs=((None, 1000), layer1_act.get_outputs()),
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
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)
#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
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
