def classify(self, single_input_as_list):
cur_input = single_input_as_list
# Make the input layer
cur_input_layer = Layer(len(cur_input), 0)
cur_input_layer.from_input_list(cur_input)
# Do forward propagation.
# Returns a list of floats
cur_model_output = self.forward_prop(cur_input_layer)
# Find the index in the output that has the highest value
val, idx = max(
(val, idx) for (idx, val) in enumerate(cur_model_output))
return idx
def train(self, example_inputs, example_outputs):
for cur_input, cur_desired_output in zip(example_inputs,
example_outputs):
# Make the input layer
cur_input_layer = Layer(len(cur_input), 0)
cur_input_layer.from_input_list(cur_input)
# Do forward propagation.
# Returns a list of floats
cur_model_output = self.forward_prop(cur_input_layer)
cur_cost_function_result = self.back_prop(cur_input_layer,
cur_model_output,
cur_desired_output)
return cur_cost_function_result
