def test_model(model, sequence_length=None, verboose=False):
input_dim = model.input_dim
output_dim = model.output_dim
batch_size = model.batch_size
I, V, sw = next(get_sample(batch_size=batch_size, in_bits=input_dim, out_bits=output_dim,
max_size=sequence_length, min_size=sequence_length))
Y = np.asarray(model.predict(I, batch_size=batch_size))
if not np.isnan(Y.sum()): #checks for a NaN anywhere
Y = (Y > 0.5).astype('float64')
x = V[:, -sequence_length:, :] == Y[:, -sequence_length:, :]
acc = x.mean() * 100
if verboose:
print("the overall accuracy for sequence_length {0} was: {1}".format(sequence_length, x.mean()))
print("per bit")
print(x.mean(axis=(0,1)))
print("per timeslot")
print(x.mean(axis=(0,2)))
else:
ntm = model.layers[0]
weights = ntm.get_weights()
import pudb; pu.db
acc = 0
return acc
def train_model(model,
epochs=10,
min_size=5,
max_size=20,
callbacks=None,
verboose=False):
input_dim = model.input_dim
output_dim = model.output_dim
batch_size = model.batch_size
sample_generator = get_sample(batch_size=batch_size,
in_bits=input_dim,
out_bits=output_dim,
max_size=max_size,
min_size=min_size)
if verboose:
for j in range(epochs):
model.fit_generator(sample_generator,
steps_per_epoch=10,
epochs=j + 1,
callbacks=callbacks,
initial_epoch=j)
print("currently at epoch {0}".format(j + 1))
for i in [5, 10, 20, 40]:
test_model(model, sequence_length=i, verboose=True)
else:
model.fit_generator(sample_generator,
steps_per_epoch=10,
epochs=epochs,
callbacks=callbacks)
print("done training")