Custom activation (2*tanh(x)) function implemented with tensorflow primitives.

network.py

@@ -18,6 +18,10 @@ class Network:
 
     # TODO: Actually compile tensorflow properly
     os.environ["TF_CPP_MIN_LOG_LEVEL"]="2"
+
+    def custom_tanh(self, x, name=None):
+        a = tf.Variable(2.00, tf.float32)
+        return tf.scalar_mul(a, tf.tanh(x, name))    
     
     def __init__(self, session):
         self.session = session
@@ -40,12 +44,10 @@ class Network:
         b_1 = tf.zeros(hidden_size,)
         b_2 = tf.zeros(output_size,)
 
-        value_after_input = tf.sigmoid(tf.matmul(self.x, W_1) + b_1, name='hidden_layer')
+        value_after_input = self.custom_tanh(tf.matmul(self.x, W_1) + b_1, name='hidden_layer')
 
         # TODO: Remember to make this tanh * 2
-      #  self.value = tf.layers.dense(input=value_after_input, units=hidden_size, \
-      #                               activation=self.custom_tanh, kernel_initializer=xavier_init())
-        self.value = 2*tf.nn.tanh(tf.matmul(value_after_input, W_2) + b_2, name='output_layer')
+        self.value = self.custom_tanh(tf.matmul(value_after_input, W_2) + b_2, name='output_layer')
 
         # tf.reduce_sum basically finds the sum of it's input, so this gives the difference between the two values, in case they should be lists, which they might be if our input changes
         difference_in_values = tf.reduce_sum(self.value_next - self.value, name='difference')
@@ -67,7 +69,6 @@ class Network:
         self.saver = tf.train.Saver(max_to_keep=1)
         self.session.run(tf.global_variables_initializer())
 
-        
     def eval_state(self, state):
         # Run state through a network