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71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from configurations import Preset
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class Architecture(object):
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def __init__(self, tuning_parameters, name=""):
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"""
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:param tuning_parameters: A Preset class instance with all the running paramaters
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:type tuning_parameters: Preset
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:param name: The name of the network
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:param name: string
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"""
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self.batch_size = tuning_parameters.batch_size
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self.input_depth = tuning_parameters.env.observation_stack_size
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self.input_height = tuning_parameters.env.desired_observation_height
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self.input_width = tuning_parameters.env.desired_observation_width
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self.num_actions = tuning_parameters.env.action_space_size
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self.measurements_size = tuning_parameters.env.measurements_size \
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if tuning_parameters.env.measurements_size else 0
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self.learning_rate = tuning_parameters.learning_rate
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self.optimizer = None
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self.name = name
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self.tp = tuning_parameters
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def get_model(self, tuning_parameters):
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"""
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:param tuning_parameters: A Preset class instance with all the running parameters
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:type tuning_parameters: Preset
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:return: A model
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"""
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pass
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def predict(self, inputs):
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pass
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def train_on_batch(self, inputs, targets):
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pass
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def get_weights(self):
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pass
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def set_weights(self, weights, rate=1.0):
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pass
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def reset_accumulated_gradients(self):
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pass
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def accumulate_gradients(self, inputs, targets):
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pass
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def apply_and_reset_gradients(self, gradients):
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pass
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def apply_gradients(self, gradients):
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pass
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