def test_build(self):
"""
Test build
"""
print("\nTest 1: RNNClassifier build")
model = RNNClassifier(2, input_dim=2, latent_dim=3)
model.build()
def test_build(self):
"""
Test build
"""
num_labels = 4
max_steps = 25
model = RNNClassifier(num_labels,
input_dim=1,
latent_dim=20,
batch_size=1,
max_steps=max_steps,
cell_class=BasicNormalTriLCell)
model.build()
def test_build(self):
"""
Test build
"""
num_labels = 4
max_steps = 25
echo_step = 3
X, Y = generateEchoData(num_labels, 10000, echo_step)
X = np.reshape(X, [-1, max_steps, 1])
Y = np.reshape(Y, [-1, max_steps, 1])
train_dataset = {'train_iseq': X[:300], 'train_labels': Y[:300]}
model = RNNClassifier(num_labels,
input_dim=1,
latent_dim=4,
batch_size=1,
max_steps=max_steps,
seq_class='lstm',
cell_class='lstm')
model.build()
model.train(train_dataset)
# Test on validation data
dataset = {'iseq': X[390:]}
Ypred = model.sample(dataset)
print(
"Preds:",
list(
zip(
np.squeeze(X[390], axis=1)[:-echo_step],
np.argmax(np.exp(Ypred[0].T) /
np.sum(np.exp(Ypred[0]), axis=1),
axis=0)[echo_step:])))
def test_init(self):
"""
Test initialization
"""
print("\nTest 0: RNNClassifier Initialization")
RNNClassifier(2, input_dim=1, latent_dim=3)
def test_build(self):
"""
Test build
"""
model = RNNClassifier(2, input_dim=1, hidden_dim=3)
model.build()
def test_init(self):
"""
Test initialization
"""
print("\nTest 0: Regression initialization")
RNNClassifier(2, input_dim=1, hidden_dim=3)