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custom_layers.py
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custom_layers.py
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import tensorflow as tf
import numpy as np
class FourierLayer(tf.keras.layers.Layer):
def __init__(self, units, scale):
super(FourierLayer, self).__init__()
self.units = units
self.scale = scale
def build(self, input_shape):
self.in_features = int(input_shape[-1])
self.B = tf.random.normal((self.in_features, self.units))
super(FourierLayer, self).build(input_shape)
def call(self, inputs):
out = tf.concat([tf.cos(self.scale * tf.matmul(inputs, self.B)), tf.sin(self.scale * tf.matmul(inputs, self.B))], axis=-1)
return out
class SineLayer(tf.keras.layers.Layer):
def __init__(self, in_features, units, bias=True, is_first=False, omega_0=30.):
super(SineLayer, self).__init__()
self.in_features = in_features
self.units = units
self.is_first = is_first
self.omega_0 = omega_0
self.dense = tf.keras.layers.Dense(self.units,
use_bias=bias,
kernel_initializer=self.init_weights(),
input_shape=(self.in_features,))
def init_weights(self):
if self.is_first:
return tf.keras.initializers.RandomUniform(minval=-1 / self.in_features,
maxval= 1 / self.in_features)
else:
return tf.keras.initializers.RandomUniform(minval=-np.sqrt(6. / self.in_features) / self.omega_0,
maxval= np.sqrt(6. / self.in_features) / self.omega_0)
def call(self, input_tensor):
befor_activation = self.dense(input_tensor)
after_activation = tf.sin(self.omega_0 * befor_activation)
return after_activation
class PureSparseLayer(tf.keras.layers.Layer):
def __init__(self, units, alpha=1, use_bias=True, activation=None, kernel_initializer=None, full="output"):
super(PureSparseLayer, self).__init__()
self.units = units
self.alpha = alpha
self.activation = activation
self.use_bias = use_bias
self.kernel_initializer = kernel_initializer
self.full = full
def build(self, input_shape):
self.in_features = int(input_shape[-1])
if self.full not in ["input", "output"]:
raise NameError('full argument must be "input" or "output"')
if self.alpha > self.in_features:
self.alpha = self.in_features
print(f"alpha set to : {self.alpha}")
n_sparse_parameters = self.alpha * self.units
if self.full == "input":
Total_Indexs = []
for_each_row = n_sparse_parameters // self.in_features
remain = n_sparse_parameters % self.in_features
remain_index = np.random.choice(self.in_features, remain, replace=False)
row_indexs = np.random.choice(self.in_features, self.in_features, replace=False)
for counter, row_index in enumerate(row_indexs):
if row_index in remain_index:
column_indexs = np.random.choice(self.units, for_each_row + 1, replace=False)
else:
column_indexs = np.random.choice(self.units, for_each_row, replace=False)
Total_Indexs.append(np.stack([row_index * np.ones_like(column_indexs), column_indexs], axis=1))
self.Total_Indexs = np.concatenate(Total_Indexs, axis=0)
elif self.full == "output":
Total_Indexs = []
for_each_column = n_sparse_parameters // self.units
remain = n_sparse_parameters % self.units
remain_index = np.random.choice(self.units, remain, replace=False)
column_indexs = np.random.choice(self.units, self.units, replace=False)
for counter, column_index in enumerate(column_indexs):
if column_index in remain_index:
row_indexs = np.random.choice(self.in_features, for_each_column + 1, replace=False)
else:
row_indexs = np.random.choice(self.in_features, for_each_column, replace=False)
Total_Indexs.append(np.stack([row_indexs, column_index * np.ones_like(row_indexs)], axis=1))
self.Total_Indexs = np.concatenate(Total_Indexs, axis=0)
else:
raise NameError('full argument must be "input" or "output"')
if self.kernel_initializer is None:
self.kernel = tf.Variable(tf.initializers.glorot_uniform()((n_sparse_parameters,)), trainable=True)
else:
self.kernel = tf.Variable(self.kernel_initializer((n_sparse_parameters,)), trainable=True)
if self.use_bias:
self.bias = tf.Variable(tf.zeros((self.units,)), trainable=True)
super(PureSparseLayer, self).build(input_shape)
def call(self, inputs):
new_kernel = tf.SparseTensor(indices=self.Total_Indexs,
values=self.kernel,
dense_shape=(self.in_features, self.units))
out = tf.sparse.sparse_dense_matmul(inputs, new_kernel)
if self.use_bias:
out = out + self.bias
if self.activation is not None:
return self.activation(out)
return out
class QuasiSparseLayer(tf.keras.layers.Layer):
def __init__(self, units, alpha=1, use_bias=True, activation=None, kernel_initializer=None, full="output"):
super(QuasiSparseLayer, self).__init__()
self.units = units
self.alpha = alpha
self.activation = activation
self.use_bias = use_bias
self.kernel_initializer = kernel_initializer
self.full = full
def build(self, input_shape):
self.in_features = int(input_shape[-1])
if self.full not in ["input", "output"]:
raise NameError('full argument must be "input" or "output"')
if self.alpha > self.in_features:
self.alpha = self.in_features
print(f"alpha set to : {self.alpha}")
n_sparse_parameters = self.alpha * self.units
if self.full == "input":
Total_Indexs = []
for_each_row = n_sparse_parameters // self.in_features
remain = n_sparse_parameters % self.in_features
remain_index = np.random.choice(self.in_features, remain, replace=False)
row_indexs = np.random.choice(self.in_features, self.in_features, replace=False)
for counter, row_index in enumerate(row_indexs):
if row_index in remain_index:
column_indexs = np.random.choice(self.units, for_each_row + 1, replace=False)
else:
column_indexs = np.random.choice(self.units, for_each_row, replace=False)
Total_Indexs.append(np.stack([row_index * np.ones_like(column_indexs), column_indexs], axis=1))
self.Total_Indexs = np.concatenate(Total_Indexs, axis=0)
elif self.full == "output":
Total_Indexs = []
for_each_column = n_sparse_parameters // self.units
remain = n_sparse_parameters % self.units
remain_index = np.random.choice(self.units, remain, replace=False)
column_indexs = np.random.choice(self.units, self.units, replace=False)
for counter, column_index in enumerate(column_indexs):
if column_index in remain_index:
row_indexs = np.random.choice(self.in_features, for_each_column + 1, replace=False)
else:
row_indexs = np.random.choice(self.in_features, for_each_column, replace=False)
Total_Indexs.append(np.stack([row_indexs, column_index * np.ones_like(row_indexs)], axis=1))
self.Total_Indexs = np.concatenate(Total_Indexs, axis=0)
else:
raise NameError('full argument must be "input" or "output"')
self.Mask = np.zeros((self.in_features, self.units))
self.Mask[self.Total_Indexs[:,0], self.Total_Indexs[:,1]] = 1
self.Mask = tf.constant(self.Mask, dtype=tf.float32)
if self.kernel_initializer is None:
self.kernel = tf.Variable(tf.initializers.glorot_uniform()((self.in_features, self.units)), trainable=True)
else:
self.kernel = tf.Variable(self.kernel_initializer((self.in_features, self.units)), trainable=True)
if self.use_bias:
self.bias = tf.Variable(tf.zeros((self.units,)), trainable=True)
super(QuasiSparseLayer, self).build(input_shape)
def call(self, inputs):
new_kernel = self.Mask * self.kernel
out = tf.matmul(inputs, new_kernel)
if self.use_bias:
out = out + self.bias
if self.activation is not None:
return self.activation(out)
return out