def test_resize(self):
output_nodes = 10
input_p = tf.placeholder("float", (None, 10))
layer = Layer(InputLayer(input_p), output_nodes, session=self.session)
layer.resize(output_nodes + 1)
print layer._bias.get_shape()
self.assertEqual(layer.activation_predict.get_shape().as_list(), [None, output_nodes + 1])
self.assertEquals(layer.output_nodes, output_nodes + 1)
def test_resize(self):
inputs = tf.placeholder(tf.float32, shape=(None, 784))
bactivate = True
net1 = InputLayer(inputs)
net2 = Layer(net1, 10, self.session, bactivate=bactivate)
bn1 = BatchNormLayer(net2, self.session)
output_net = Layer(bn1, 10, self.session, bactivate=False)
print(self.session.run(output_net.activation_predict, feed_dict={inputs: np.zeros(shape=(1, 784))}))
net2.resize(net2.output_nodes + 1)
print(self.session.run(output_net.activation_predict, feed_dict={inputs: np.zeros(shape=(1, 784))}))
def test_reshape(self):
output_nodes = 2
input_p = tf.placeholder("float", (None, 2))
layer = Layer(InputLayer(input_p), output_nodes, session=self.session,
weights=np.array([[100.0]], dtype=np.float32))
result1 = self.session.run(layer.activation_predict, feed_dict={layer.input_placeholder: [[1., 1.]]})
layer.resize(3)
result2 = self.session.run(layer.activation_predict, feed_dict={layer.input_placeholder: [[1., 1.]]})
print(result1)
print(result2)
self.assertEquals(len(result2[0]), 3)
