def __init__(self, x_len, h_len, y_len, inner_activation, out_activation, sequence_len):
self.head = RnnLayer(x_len, h_len, y_len, inner_activation, out_activation)
self.w_xh, self.w_hh, self.w_hy, self.w_by, self.w_bh = self.head.create_w()
# link together
prev = self.head
self.sequence_len = sequence_len
for i in range(self.sequence_len - 1):
layer = RnnLayer(x_len, h_len, y_len, inner_activation, out_activation, self.w_xh, self.w_hh, self.w_hy, self.w_by, self.w_bh)
prev.set_post(layer)
layer.set_prev(prev)
prev = layer
self.end = prev
def __init__(self, x_len, h_len, y_len, inner_activation, out_activation,
sequence_len):
self.head = RnnLayer(x_len, h_len, y_len, inner_activation,
out_activation)
self.w_xh, self.w_hh, self.w_hy, self.w_by, self.w_bh = self.head.create_w(
)
# link together
prev = self.head
self.sequence_len = sequence_len
for i in range(self.sequence_len - 1):
layer = RnnLayer(x_len, h_len, y_len, inner_activation,
out_activation, self.w_xh, self.w_hh, self.w_hy,
self.w_by, self.w_bh)
prev.set_post(layer)
layer.set_prev(prev)
prev = layer
self.end = prev
class RnnNet(object):
def __init__(self, x_len, h_len, y_len, inner_activation, out_activation, sequence_len):
self.head = RnnLayer(x_len, h_len, y_len, inner_activation, out_activation)
self.w_xh, self.w_hh, self.w_hy, self.w_by, self.w_bh = self.head.create_w()
# link together
prev = self.head
self.sequence_len = sequence_len
for i in range(self.sequence_len - 1):
layer = RnnLayer(x_len, h_len, y_len, inner_activation, out_activation, self.w_xh, self.w_hh, self.w_hy, self.w_by, self.w_bh)
prev.set_post(layer)
layer.set_prev(prev)
prev = layer
self.end = prev
def feed_forward(self, data):
if len(data) != self.sequence_len:
print '[Warning] Input length not match sequence number!'
cur_layer = self.head
for i in xrange(len(data)):
cur_layer.tiny_forward(data[i])
cur_layer = cur_layer.get_post()
def back_propagate(self, teacher_data, lr):
if len(teacher_data) != self.sequence_len:
print '[Warning] Input teacher length not match sequence number!'
cur_layer = self.end
post_h_delta = 0
for i in reversed(xrange(len(teacher_data))):
post_h_delta = cur_layer.tiny_backward(post_h_delta, teacher_data[i])
cur_layer = cur_layer.get_prev()
# finally update weights
self.head.update_weights(lr)
def xentroty_err(self, teacher_data):
cur_layer = self.head
error = 0
i = 0
while cur_layer is not None and i < len(teacher_data):
error += cur_layer.xentropy(teacher_data[i])
cur_layer = cur_layer.post
i += 1
return error
def get_outputs(self):
cur_layer = self.head
mat = np.zeros((self.sequence_len, len(cur_layer.y)))
i = 0
while cur_layer is not None:
mat[i] = cur_layer.y
cur_layer = cur_layer.post
i += 1
return mat
def sample(self, init_x, char_arr, char_map):
cur_layer = self.head
result = []
while cur_layer is not None:
cur_layer.tiny_forward(init_x)
cc = m2t([cur_layer.y], char_arr)
result.append(cc)
init_x.fill(0)
init_x[char_map[cc]] = 1
cur_layer = cur_layer.post
return "".join(result)
class RnnNet(object):
def __init__(self, x_len, h_len, y_len, inner_activation, out_activation,
sequence_len):
self.head = RnnLayer(x_len, h_len, y_len, inner_activation,
out_activation)
self.w_xh, self.w_hh, self.w_hy, self.w_by, self.w_bh = self.head.create_w(
)
# link together
prev = self.head
self.sequence_len = sequence_len
for i in range(self.sequence_len - 1):
layer = RnnLayer(x_len, h_len, y_len, inner_activation,
out_activation, self.w_xh, self.w_hh, self.w_hy,
self.w_by, self.w_bh)
prev.set_post(layer)
layer.set_prev(prev)
prev = layer
self.end = prev
def feed_forward(self, data):
if len(data) != self.sequence_len:
print '[Warning] Input length not match sequence number!'
cur_layer = self.head
for i in xrange(len(data)):
cur_layer.tiny_forward(data[i])
cur_layer = cur_layer.get_post()
def back_propagate(self, teacher_data, lr):
if len(teacher_data) != self.sequence_len:
print '[Warning] Input teacher length not match sequence number!'
cur_layer = self.end
post_h_delta = 0
for i in reversed(xrange(len(teacher_data))):
post_h_delta = cur_layer.tiny_backward(post_h_delta,
teacher_data[i])
cur_layer = cur_layer.get_prev()
# finally update weights
self.head.update_weights(lr)
def xentroty_err(self, teacher_data):
cur_layer = self.head
error = 0
i = 0
while cur_layer is not None and i < len(teacher_data):
error += cur_layer.xentropy(teacher_data[i])
cur_layer = cur_layer.post
i += 1
return error
def get_outputs(self):
cur_layer = self.head
mat = np.zeros((self.sequence_len, len(cur_layer.y)))
i = 0
while cur_layer is not None:
mat[i] = cur_layer.y
cur_layer = cur_layer.post
i += 1
return mat
def sample(self, init_x, char_arr, char_map):
cur_layer = self.head
result = []
while cur_layer is not None:
cur_layer.tiny_forward(init_x)
cc = m2t([cur_layer.y], char_arr)
result.append(cc)
init_x.fill(0)
init_x[char_map[cc]] = 1
cur_layer = cur_layer.post
return "".join(result)
