def test_bug_issue_1(self):
non_liniarity = tf.nn.relu
regularizer_coeff = 0.01
last_layer = InputLayer(self.mnist_data.features_shape,
# drop_out_prob=.5,
layer_noise_std=1.
)
last_layer = HiddenLayer(last_layer, 100, self.session, non_liniarity=non_liniarity,
batch_normalize_input=True)
output = CategoricalOutputLayer(last_layer, self.mnist_data.labels_shape, self.session,
batch_normalize_input=True,
regularizer_weighting=regularizer_coeff)
output.train_till_convergence(self.mnist_data.train, self.mnist_data.validation,
learning_rate=.1)
last_layer.resize(110)
output.train_till_convergence(self.mnist_data.train, self.mnist_data.validation,
learning_rate=.1)
last_layer.resize(90)
output.train_till_convergence(self.mnist_data.train, self.mnist_data.validation,
learning_rate=.1)
def test_bug_issue_2(self):
last_layer = InputLayer((1,))
last_layer = HiddenLayer(last_layer, 2, self.session,
batch_normalize_input=True)
output = CategoricalOutputLayer(last_layer, (1,), self.session,
batch_normalize_input=True)
print output.activate_predict([[0.]])
last_layer.resize(4)
print output.activate_predict([[0.]])
def test_adding_hidden_layer_with_resize(self):
non_liniarity = tf.nn.relu
regularizer_coeff = None
layer = InputLayer(self.mnist_data.features_shape)
layer = HiddenLayer(layer, 100, self.session, non_liniarity=non_liniarity,
batch_normalize_input=False)
output = CategoricalOutputLayer(layer, self.mnist_data.labels_shape, self.session,
batch_normalize_input=True,
regularizer_weighting=regularizer_coeff)
output.train_till_convergence(self.mnist_data.train, self.mnist_data.validation,
learning_rate=.1)
layer.add_intermediate_cloned_layer()
layer.resize(110)
self.session.run(output.activation_predict,
feed_dict={output.input_placeholder: self.mnist_data.train.features[:3],
output.target_placeholder: self.mnist_data.train.labels[:3]})
def test_bug_issue_with_state(self):
non_liniarity = tf.nn.relu
regularizer_coeff = 0.01
layer = InputLayer(self.mnist_data.features_shape, layer_noise_std=1.)
layer = HiddenLayer(layer, 6, self.session, non_liniarity=non_liniarity,
batch_normalize_input=True)
output = CategoricalOutputLayer(layer, self.mnist_data.labels_shape, self.session,
batch_normalize_input=True,
regularizer_weighting=regularizer_coeff)
state = output.get_network_state()
layer.resize(10)
output.train_till_convergence(self.mnist_data.train, self.mnist_data.validation,
learning_rate=.1)
output.set_network_state(state)
output.train_till_convergence(self.mnist_data.train, self.mnist_data.validation,
learning_rate=.1)