Before defining our cost function, we need to define how long we are going to train and how we should define the learning rate:
#Number of training epochsnum_epochs = 700# Defining our learning rate iterations (decay)learning_rate = tf.train.exponential_decay(learning_rate=0.0008, global_step=1, decay_steps=train_input_values.shape[0], decay_rate=0.95, staircase=True)# Defining our cost function - Squared Mean Errormodel_cost = tf.nn.l2_loss(activation_output - output_values, name="squared_error_cost")# Defining our Gradient Descentmodel_train = tf.train.GradientDescentOptimizer(learning_rate).minimize(model_cost)
Now, it's time to execute our computational graph through the session variable.
So first off, we need to initialize ...
