def __init__(self,
lid,
par,
bacth_size,
num_lstm,
dim_frame,
l2_decay,
use_th=True,
activation='tanh',
inner_activation='sigmoid',
**kwargs):
self.num_lstm = num_lstm
self.dim_frame = dim_frame
self.batch_size = bacth_size
self.inner_activation = activations.get(inner_activation)
self.activation = activations.get(activation)
self.lstmpar = par
self.l2_decay = l2_decay
self.use_th = use_th
self.bn = BatchNormalization(lid, self.batch_size, self.num_lstm)
self.bn.build()
self.id = lid
kwargs['input_shape'] = (self.batch_size, self.num_lstm)
super(LSTM_Dec, self).__init__(**kwargs)
def __init__(self, *outshape, **kargs):
#输出形状
if len(outshape) == 1 and type(outshape[0]) == type(()):
self.__outshape = outshape[0]
else:
self.__outshape = outshape
#输入形状
self.__inshape = None
#得到激活函数
self.__activation = activations.get('linear')
#层在模型中的id, 是层在模型中的索引
self.__id = 0
#层的名字
self.__name = '/%d-%s'%(self.__id, self.tag)
#得到可选参数
#print("Layer kargs:", kargs)
if 'inshape' in kargs:
self.__inshape = kargs['inshape']
if type(self.__inshape) != type(()):
self.__inshape = (self.__inshape,)
#print("------inshape:", self.__inshape)
if 'activation' in kargs:
self.__activation = activations.get(kargs['activation'])
if self.__inshape is not None:
self.init_params()
def __init__(self, lid, par, bacth_size, num_lstm, dim_frame, l2_decay, activation='tanh', inner_activation='sigmoid', **kwargs):
self.num_lstm = num_lstm
self.dim_frame = dim_frame
self.has_input_frame = True
self.batch_size = bacth_size
self.inner_activation = activations.get(inner_activation)
self.activation = activations.get(activation)
self.bn = BatchNormalization(lid, self.batch_size, self.num_lstm)
self.bn.build()
self.lstmpar = par
self.l2_decay = l2_decay
self.id = lid
kwargs['input_shape'] = (self.batch_size,self.num_lstm)
super(LSTM_Unit, self).__init__(**kwargs)
def __init__(self, in_dim, name=None, init='uniform', activation='linear'):
if name is not None:
self.set_name(name)
self.name = name
self.in_dim = in_dim
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.init_params()
def __init__(self, activation='tanh'):
self.__activation = activations.get(activation)
self.__memories = []
self.__forgets = []
self.__inputs = []
self.__mcs = []
self.__pre_mem = None
self.__pre_mem_grad = None
def __init__(self, input_dim, output_dim=128,
init='uniform', inner_init='orthogonal',
activation='tanh', inner_activation='hard_sigmoid',
truncate_gradient=-1, weights=None, return_sequences=False):
self.input_dim = input_dim
self.output_dim = output_dim
self.truncate_gradient = truncate_gradient
self.return_sequences = return_sequences
self.init = initializations.get(init)
self.inner_init = initializations.get(inner_init)
self.activation = activations.get(activation)
self.inner_activation = activations.get(inner_activation)
self.input = T.matrix()
self.W_i = self.init((self.input_dim, self.output_dim))
self.U_i = self.inner_init((self.output_dim, self.output_dim))
self.b_i = shared_zeros((self.output_dim))
self.W_f = self.init((self.input_dim, self.output_dim))
self.U_f = self.inner_init((self.output_dim, self.output_dim))
self.b_f = shared_zeros((self.output_dim))
self.W_c = self.init((self.input_dim, self.output_dim))
self.U_c = self.inner_init((self.output_dim, self.output_dim))
self.b_c = shared_zeros((self.output_dim))
self.W_o = self.init((self.input_dim, self.output_dim))
self.U_o = self.inner_init((self.output_dim, self.output_dim))
self.b_o = shared_zeros((self.output_dim))
self.params = [
self.W_i, self.U_i, self.b_i,
self.W_c, self.U_c, self.b_c,
self.W_f, self.U_f, self.b_f,
self.W_o, self.U_o, self.b_o,
]
if weights is not None:
self.set_weights(weights)
def __init__(self, input_dim, output_dim, depth=3,
init='uniform', inner_init='orthogonal',
activation='sigmoid', inner_activation='hard_sigmoid',
weights=None, truncate_gradient=-1, return_sequences=False):
self.init = initializations.get(init)
self.inner_init = initializations.get(inner_init)
self.input_dim = input_dim
self.output_dim = output_dim
self.truncate_gradient = truncate_gradient
self.activation = activations.get(activation)
self.inner_activation = activations.get(inner_activation)
self.depth = depth
self.return_sequences = return_sequences
self.input = T.matrix()
self.W = self.init((self.input_dim, self.output_dim))
self.Us = [self.init((self.output_dim, self.output_dim)) for _ in range(self.depth)]
self.b = shared_zeros((self.output_dim))
self.params = [self.W] + self.Us + [self.b]
if weights is not None:
self.set_weights(weights)
def __init__(self, input_dim, output_dim, init='uniform', activation='linear', weights=None):
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.input_dim = input_dim
self.output_dim = output_dim
self.input = T.matrix()
self.W = self.init((self.input_dim, self.output_dim))
self.b = shared_zeros((self.output_dim))
self.params = [self.W, self.b]
if weights is not None:
self.set_weights(weights)
def __init__(self,
in_dim,
out_dim,
name=None,
init='xavier',
activation='relu'):
if name is not None:
self.set_name(name)
self.name = name
self.in_dim = in_dim
self.out_dim = out_dim
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.init_params()
def __init__(self, input_n, output_n, init='glorot_uniform', activation='linear'): super(FullyConnected, self).__init__() self.input_n = input_n self.output_n = output_n self.init = initializations.get(init) self.activation = activations.get(activation) self.input = T.matrix() self.W = self.init((self.input_n, self.output_n)) self.b = shared_zeros((self.output_n)) self.params = [self.W, self.b]
def __init__(self, activation='linear'):
#得到激活函数
self.__activation = activations.get(activation)
#当前层子层的id seed
self.__child_id_seed = 1
#父层
self.__parent = None
#层在模型中的id, 当前在父层中的索引
self.__id = 0
#层的名字
self.__name = self.tag
#上一个层
self.__prev = None
def __init__(self,
output_size,
input_shape,
activation='linear',
bias_type="per_node",
trainable=True,
weight_initializer="uniform",
bias_initializer="uniform") -> None:
"""
The Dense layer is one of the most simplest layers.
Arguments:
output_size : int : The number of outputs for this layer.
input_shape : tuple/int : The shape (excluding batch size) of the input data this layer will receive.
activation : Base_Activation/str : A mathematical function that generally applies a non-linear mapping to some data.
bias_type : str : Has three settings, per_node (a bias with weights for every output),
- single (one bias weight for all outputs), none (no bias is used).
trainable : bool : If True, the vars in this layer will update based on the calculated loss of this layer W.R.T the vars.
weight_initializer : str : An initializer function to set the values for the weights.
bias_initializer : st : An initializer function to set the values for the bias'.
"""
super().__init__('Dense')
# Flattens the input_shape if data type is tuple.
if type(input_shape) == tuple:
input_shape = len(np.zeros(input_shape).flatten())
self.built = False
self.layer_shape = (output_size, input_shape)
self.bias_type = bias_type
self.trainable = trainable
self.activation = a.get(activation)
self.weights = 0
self.bias = 0
self.weight_initializer = weight_initializer
self.bias_initializer = bias_initializer
self.optimizations = {"weight": [0, 0], "bias": [0, 0]}
self.cached_data = {}
def __init__(self, nb_filter, stack_size, nb_row, nb_col,
init='uniform', activation='linear', weights=None,
image_shape=None, border_mode='valid', subsample=(1,1)):
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.subsample = subsample
self.border_mode = border_mode
self.image_shape = image_shape
self.input = T.tensor4()
self.W_shape = (nb_filter, stack_size, nb_row, nb_col)
self.W = self.init(self.W_shape)
self.b = shared_zeros((nb_filter,))
self.params = [self.W, self.b]
if weights is not None:
self.set_weights(weights)
def __init__(self, layer_sizes, activation, cost, reg_lambda=0.01):
'''
Arguments:
layer_sizes {list} -- Initialize NN with number of layers and number of units per layer.
Takes list of numbers. The size of the list indicate number of layers.
Each number in the list indicates number of units in each layer.
Keyword Arguments:
reg_lambda {float} -- regularization lambda value (default: {0.01})
dropout_p {float} -- probability value for dropouts (default: {0.5})
'''
self.num_layers = len(layer_sizes)
self.layer_sizes = layer_sizes
self.activation = activations.get(activation)
self.reg_lambda = reg_lambda
self.cost = cost_funcs.get(cost)
self.biases = [np.random.randn(y, 1) for y in layer_sizes[1:]]
self.weights = [
np.random.randn(y, x)
for x, y in zip(layer_sizes[:-1], layer_sizes[1:])
]
def load(self, layer_data) -> None:
"""
Takes the layer_data from the model this layer belongs to, and sets all vars equal to each key in layer_data.
Arguments:
layer_data : dict : A dictonary of saved vars from when this layer was first built and then saved.
"""
self.name = layer_data["name"]
self.built = layer_data["built"]
self.layer_shape = tuple(layer_data["layer_shape"])
self.bias_type = layer_data["bias_type"]
self.trainable = layer_data["trainable"]
self.activation = a.get(layer_data["activation"])
self.weight_initializer = layer_data["weight_initializer"]
self.bias_initializer = layer_data["bias_initializer"]
self.weights = np.asarray(layer_data["weights"])
self.bias = np.asarray(layer_data["bias"])
self.optimizations = {
key: np.asarray(layer_data["optimizations"][key])
for key in layer_data["optimizations"]
}
def __init__(self, activation): super(Activation, self).__init__() self.activation = activations.get(activation)
def __init__(self, activation):
self.activation = activations.get(activation)
self.params = []
def __init__(self, output_size, input_shape, activation="relu", bias_type="per_node",
trainable=True, filter_size=(1,1), stride=(1,1), padding=0,
weight_initializer="random", bias_initializer="random") -> None:
"""
The conv layer or convolutional layer creates a numpy array that is convolved or cross-correlated over some data.
The conv layer also makes use of shared variables, meaning that compared to a dense layer there will be less variables.
Arguments:
output_size : int : An int of the output size, (E.X output_size=6 returns a numpy array of size
- [batch_size, ..., 6] "an image with 6 channels").
input_shape : tuple : A tuple of the input shape, (E.X input_shape=(28, 28, 1) which in this example is a 28*28 image with 1 channel).
activation : Base_Activation/str : A mathematical function that generally applies a non-linear mapping to some data.
bias_type : str : Has three settings, per_node (a bias with weights for every output),
- single (one bias weight for all outputs), none (no bias is used).
trainable : bool : If True, the vars in this layer will update based on the calculated loss of this layer W.R.T the vars.
filter_size : tuple/int : A tuple of 2 values or a int specifying the height and width of each segment of data that will be convolved over.
stride : tuple/int : A tuple or int of values specifying the stride for the height and width when convolving over some data.
padding : int : An int specifying the amount of padding to be added around the input data.
weight_initializer : str : An initializer function to set the values for the weights.
bias_initializer : st : An initializer function to set the values for the bias'.
"""
super().__init__("Conv")
if len(input_shape) == 2:
if channels_first:
input_shape = (1, input_shape[0], input_shape[1])
else:
input_shape = (input_shape[0], input_shape[1], 1)
if isinstance(filter_size, int):
filter_size = (filter_size, filter_size)
if isinstance(stride, int):
stride = (stride, stride)
self.built = False
self.layer_shape = (output_size, input_shape)
self.activation = a.get(activation)
self.bias_type = bias_type
self.trainable = trainable
self.filter_size = filter_size
self.stride = stride
self.padding = padding
self.weight_initializer = weight_initializer
self.bias_initializer = bias_initializer
self.weights = 0
self.bias = 0
self.optimizations = {"weight":[0,0], "bias":[0,0]}
self.cached_data = {}
# Put this into it's own function called get_ouput_size()
# calculates the output sizes for the layer.
height_output_size = int((self.layer_shape[1][0]-self.filter_size[0] + (2*self.padding))/self.stride[0]) + 1
width_output_size = int((self.layer_shape[1][1]-self.filter_size[1] + (2*self.padding))/self.stride[1]) + 1
self.output_shape = (height_output_size, width_output_size, output_size)