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3_RNN/Initializers.py

+import numpy as np
+
+
+class Constant:
+
+    def __init__(self, constant=0.1):
+        self.constant = constant
+
+    def initialize(self, weights_shape, fan_in, fan_out):
+        output = (np.ones(weights_shape))*self.constant
+
+        return output
+
+
+class UniformRandom:
+
+    def __init__(self):
+        self.low = None
+        self.high = None
+
+    def initialize(self, weights_shape, fan_in, fan_out):
+        self.low = 0
+        self.high = 1
+        output = np.random.uniform(self.low, self.high, weights_shape)
+        return output
+
+
+class Xavier:
+
+    def __init__(self):
+        self.std = None
+
+    def initialize(self, weights_shape, fan_in, fan_out):
+        self.std = np.sqrt(2 / (fan_in + fan_out))
+        output = np.random.normal(0, self.std, weights_shape)
+
+        return output
+
+
+class He:
+
+    def __init__(self):
+        self.std = None
+
+    def initialize(self, weights_shape, fan_in, fan_out):
+        self.std = np.sqrt(2 / fan_in)
+        output = np.random.normal(0, self.std, weights_shape)
+
+        return output