def ffnet():
inputs = 2
outputs = 2
layers = 2
hidden_dimension = 50
# input variables denoting the features and label data
features = C.input_variable((inputs), np.float32)
label = C.input_variable((outputs), np.float32)
# Instantiate the feedforward classification model
my_model = Sequential(
[Dense(hidden_dimension, activation=C.sigmoid),
Dense(outputs)])
z = my_model(features)
ce = C.cross_entropy_with_softmax(z, label)
pe = C.classification_error(z, label)
# Instantiate the trainer object to drive the model training
lr_per_minibatch = learning_rate_schedule(0.125, UnitType.minibatch)
trainer = C.Trainer(z, ce, pe, [sgd(z.parameters, lr=lr_per_minibatch)])
# Get minibatches of training data and perform model training
minibatch_size = 25
num_minibatches_to_train = 1024
pp = ProgressPrinter(0)
for i in range(num_minibatches_to_train):
train_features, labels = generate_random_data(minibatch_size, inputs,
outputs)
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
trainer.train_minibatch({features: train_features, label: labels})
pp.update_with_trainer(trainer)
last_avg_error = pp.avg_loss_since_start()
test_features, test_labels = generate_random_data(minibatch_size, inputs,
outputs)
avg_error = trainer.test_minibatch({
features: test_features,
label: test_labels
})
print(' error rate on an unseen minibatch: {}'.format(avg_error))
return last_avg_error, avg_error
def ffnet():
inputs = 2
outputs = 2
layers = 2
hidden_dimension = 50
# input variables denoting the features and label data
features = C.input_variable((inputs), np.float32)
label = C.input_variable((outputs), np.float32)
# Instantiate the feedforward classification model
my_model = Sequential ([
Dense(hidden_dimension, activation=C.sigmoid),
Dense(outputs)])
z = my_model(features)
ce = C.cross_entropy_with_softmax(z, label)
pe = C.classification_error(z, label)
# Instantiate the trainer object to drive the model training
lr_per_minibatch = learning_rate_schedule(0.125, UnitType.minibatch)
trainer = C.Trainer(z, (ce, pe), [sgd(z.parameters, lr=lr_per_minibatch)])
# Get minibatches of training data and perform model training
minibatch_size = 25
num_minibatches_to_train = 1024
pp = ProgressPrinter(0)
for i in range(num_minibatches_to_train):
train_features, labels = generate_random_data(minibatch_size, inputs, outputs)
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
trainer.train_minibatch({features : train_features, label : labels})
pp.update_with_trainer(trainer)
last_avg_error = pp.avg_loss_since_start()
test_features, test_labels = generate_random_data(minibatch_size, inputs, outputs)
avg_error = trainer.test_minibatch({features : test_features, label : test_labels})
print(' error rate on an unseen minibatch: {}'.format(avg_error))
return last_avg_error, avg_error
