Page 68 - MSDN Magazine, March 2018

Figure 3 Demo Program Structure
# cleveland_bnn.py
# CNTK 2.3 with Anaconda 4.1.1 (Python 3.5, NumPy 1.11.1)
import numpy as np import cntk as C
def create_reader(path, input_dim, output_dim, rnd_order, sweeps): x_strm = C.io.StreamDef(field='symptoms', shape=input_dim,
is_sparse=False)
y_strm = C.io.StreamDef(field='disease', shape=output_dim,
is_sparse=False)
streams = C.io.StreamDefs(x_src=x_strm, y_src=y_strm) deserial = C.io.CTFDeserializer(path, streams)
mb_src = C.io.MinibatchSource(deserial, randomize=rnd_order, \
max_sweeps=sweeps) return mb_src
# ===================================================================
def main():
print("\nBegin binary classification (two-node technique) \n") print("Using CNTK version = " + str(C.__version__) + "\n")
input_dim = 18 hidden_dim = 20 output_dim = 2
train_file = ".\\Data\\cleveland_cntk_twonode.txt"
# 1. create network
X = C.ops.input_variable(input_dim, np.float32) Y = C.ops.input_variable(output_dim, np.float32)
print("Creating a 18-20-2 tanh-softmax NN ")
with C.layers.default_options(init=C.initializer.uniform(scale=0.01,\
seed=1)):
hLayer = C.layers.Dense(hidden_dim, activation=C.ops.tanh,
name='hidLayer')(X)
oLayer = C.layers.Dense(output_dim, activation=None,
name='outLayer')(hLayer) nnet = oLayer
model = C.ops.softmax(nnet)
# 2. create learner and trainer
print("Creating a cross entropy batch=10 SGD LR=0.005 Trainer ") tr_loss = C.cross_entropy_with_softmax(nnet, Y)
tr_clas = C.classification_error(nnet, Y)
max_iter = 5000 batch_size = 10
learn_rate = 0.005
learner = C.sgd(nnet.parameters, learn_rate)
trainer = C.Trainer(nnet, (tr_loss, tr_clas), [learner])
# 3. create reader for train data
rdr = create_reader(train_file, input_dim, output_dim,
rnd_order=True, sweeps=C.io.INFINITELY_REPEAT) heart_input_map = {
X : rdr.streams.x_src,
Y : rdr.streams.y_src }
# 4. train
print("\nStarting training \n") for i in range(0, max_iter):
curr_batch = rdr.next_minibatch(batch_size, \ input_map=heart_input_map)
trainer.train_minibatch(curr_batch) if i % int(max_iter/10) == 0:
mcee = trainer.previous_minibatch_loss_average
macc = (1.0 - trainer.previous_minibatch_evaluation_average) \
* 100
print("batch %4d: mean loss = %0.4f, accuracy = %0.2f%% " \
% (i, mcee, macc)) print("\nTraining complete")
# 5. evaluate model using all data
print("\nEvaluating accuracy using built-in test_minibatch() \n") rdr = create_reader(train_file, input_dim, output_dim,
rnd_order=False, sweeps=1) heart_input_map = {
X : rdr.streams.x_src,
Y : rdr.streams.y_src }
num_test = 297
all_test = rdr.next_minibatch(num_test, input_map=heart_input_map) acc = (1.0 - trainer.test_minibatch(all_test)) * 100 print("Classification accuracy on the %d data items = %0.2f%%" \
% (num_test,acc))
# (could save model here)
# (use trained model to make prediction)
print("\nEnd Cleveland Heart Disease classification ")
# ===================================================================
if __name__ == "__main__": main()
A reader object is created with these statements:
rdr = create_reader(train_file, input_dim, output_dim, rnd_order=True, sweeps=C.io.INFINITELY_REPEAT)
heart_input_map = {
X : rdr.streams.x_src, Y : rdr.streams.y_src
}
If you examine the create_reader definition in Figure 3, you’ll see that it specifies the tag names (“symptoms” and “disease”) used in the data file. You can consider create_reader and the code to create a reader object as boilerplate code for neural binary classification problems. All you have to change are the tag names and the name of the mapping dictionary (heart_input_map).
After everything is prepared, training is performed like so:
for i in range(0, max_iter):
curr_batch = rdr.next_minibatch(batch_size, \
input_map=heart_input_map) trainer.train_minibatch(curr_batch) if i % int(max_iter/10) == 0:
mcee = trainer.previous_minibatch_loss_average
macc = (1.0 - trainer.previous_minibatch_evaluation_average) \
* 100
print("batch %4d: mean loss = %0.4f, accuracy = %0.2f%% " \
% (i, mcee, macc))
An alternative to training with a fixed number of iterations is to stop training when loss/error drops below a threshold. It’s import- ant to display loss/error during training because training failure is the rule rather than the exception. Cross-entropy error is a bit dif- ficult to interpret directly, but you want to see values that tend to get smaller. Instead of displaying average classification loss/error, the demo computes and prints the average classification accuracy, which is a more natural metric in my opinion.
Evaluating and Using the Model
After a network has been trained, you’ll usually want to determine the loss/error and classification accuracy for the entire dataset that was used for training. The demo evaluates overall classifica- tion accuracy with:
rdr = create_reader(train_file, input_dim, output_dim, rnd_order=False, sweeps=1)
heart_input_map = {
X : rdr.streams.x_src, Y : rdr.streams.y_src
}
num_test = 297
62 msdn magazine
Test Run