Python Model.serialize方法代码示例

# 需要导入模块: from neon.models import Model [as 别名]
# 或者: from neon.models.Model import serialize [as 别名]
def test_model_serialize(backend_default, data):
    (X_train, y_train), (X_test, y_test), nclass = load_mnist(path=data)

    train_set = DataIterator(
        [X_train, X_train], y_train, nclass=nclass, lshape=(1, 28, 28))

    init_norm = Gaussian(loc=0.0, scale=0.01)

    # initialize model
    path1 = Sequential([Conv((5, 5, 16), init=init_norm, bias=Constant(0), activation=Rectlin()),
                        Pooling(2),
                        Affine(nout=20, init=init_norm, bias=init_norm, activation=Rectlin())])
    path2 = Sequential([Affine(nout=100, init=init_norm, bias=Constant(0), activation=Rectlin()),
                        Dropout(keep=0.5),
                        Affine(nout=20, init=init_norm, bias=init_norm, activation=Rectlin())])
    layers = [MergeMultistream(layers=[path1, path2], merge="stack"),
              Affine(nout=20, init=init_norm, batch_norm=True, activation=Rectlin()),
              Affine(nout=10, init=init_norm, activation=Logistic(shortcut=True))]

    tmp_save = 'test_model_serialize_tmp_save.pickle'
    mlp = Model(layers=layers)
    mlp.optimizer = GradientDescentMomentum(learning_rate=0.1, momentum_coef=0.9)
    mlp.cost = GeneralizedCost(costfunc=CrossEntropyBinary())
    mlp.initialize(train_set, cost=mlp.cost)
    n_test = 3
    num_epochs = 3
    # Train model for num_epochs and n_test batches
    for epoch in range(num_epochs):
        for i, (x, t) in enumerate(train_set):
            x = mlp.fprop(x)
            delta = mlp.cost.get_errors(x, t)
            mlp.bprop(delta)
            mlp.optimizer.optimize(mlp.layers_to_optimize, epoch=epoch)
            if i > n_test:
                break

    # Get expected outputs of n_test batches and states of all layers
    outputs_exp = []
    pdicts_exp = [l.get_params_serialize() for l in mlp.layers_to_optimize]
    for i, (x, t) in enumerate(train_set):
        outputs_exp.append(mlp.fprop(x, inference=True))
        if i > n_test:
            break

    # Serialize model
    save_obj(mlp.serialize(keep_states=True), tmp_save)

    # Load model
    mlp = Model(layers=layers)
    mlp.load_weights(tmp_save)

    outputs = []
    pdicts = [l.get_params_serialize() for l in mlp.layers_to_optimize]
    for i, (x, t) in enumerate(train_set):
        outputs.append(mlp.fprop(x, inference=True))
        if i > n_test:
            break

    # Check outputs, states, and params are the same
    for output, output_exp in zip(outputs, outputs_exp):
        assert np.allclose(output.get(), output_exp.get())

    for pd, pd_exp in zip(pdicts, pdicts_exp):
        for s, s_e in zip(pd['states'], pd_exp['states']):
            if isinstance(s, list):  # this is the batch norm case
                for _s, _s_e in zip(s, s_e):
                    assert np.allclose(_s, _s_e)
            else:
                assert np.allclose(s, s_e)
        for p, p_e in zip(pd['params'], pd_exp['params']):
            assert type(p) == type(p_e)
            if isinstance(p, list):  # this is the batch norm case
                for _p, _p_e in zip(p, p_e):
                    assert np.allclose(_p, _p_e)
            elif isinstance(p, np.ndarray):
                assert np.allclose(p, p_e)
            else:
                assert p == p_e

    os.remove(tmp_save)

# 需要导入模块: from neon.models import Model [as 别名]
# 或者: from neon.models.Model import serialize [as 别名]
class DeepQNetwork:
  def __init__(self, num_actions, args):
    # create Neon backend
    self.be = gen_backend(backend = args.backend,
                 batch_size = args.batch_size,
                 rng_seed = args.random_seed,
                 device_id = args.device_id,
                 default_dtype = np.dtype(args.datatype).type,
                 stochastic_round = args.stochastic_round)

    # create model
    layers = self.createLayers(num_actions)
    self.model = Model(layers = layers)
    self.cost = GeneralizedCost(costfunc = SumSquared())
    self.optimizer = RMSProp(learning_rate = args.learning_rate, 
        decay_rate = args.rmsprop_decay_rate, 
        stochastic_round = args.stochastic_round)

    # create target model
    self.target_steps = args.target_steps
    self.train_iterations = 0
    if self.target_steps:
      self.target_model = Model(layers = self.createLayers(num_actions))
      self.save_weights_path = args.save_weights_path
    else:
      self.target_model = self.model

    # remember parameters
    self.num_actions = num_actions
    self.batch_size = args.batch_size
    self.discount_rate = args.discount_rate
    self.history_length = args.history_length
    self.screen_dim = (args.screen_height, args.screen_width)
    self.clip_error = args.clip_error

    # prepare tensors once and reuse them
    self.input_shape = (self.history_length,) + self.screen_dim + (self.batch_size,)
    self.tensor = self.be.empty(self.input_shape)
    self.tensor.lshape = self.input_shape # needed for convolutional networks
    self.targets = self.be.empty((self.num_actions, self.batch_size))

    self.callback = None

  def createLayers(self, num_actions):
    # create network
    init_norm = Gaussian(loc=0.0, scale=0.01)
    layers = []
    # The first hidden layer convolves 32 filters of 8x8 with stride 4 with the input image and applies a rectifier nonlinearity.
    layers.append(Conv((8, 8, 32), strides=4, init=init_norm, activation=Rectlin()))
    # The second hidden layer convolves 64 filters of 4x4 with stride 2, again followed by a rectifier nonlinearity.
    layers.append(Conv((4, 4, 64), strides=2, init=init_norm, activation=Rectlin()))
    # This is followed by a third convolutional layer that convolves 64 filters of 3x3 with stride 1 followed by a rectifier.
    layers.append(Conv((3, 3, 64), strides=1, init=init_norm, activation=Rectlin()))
    # The final hidden layer is fully-connected and consists of 512 rectifier units.
    layers.append(Affine(nout=512, init=init_norm, activation=Rectlin()))
    # The output layer is a fully-connected linear layer with a single output for each valid action.
    layers.append(Affine(nout = num_actions, init = init_norm))
    return layers

  def setTensor(self, states):
    # change order of axes to match what Neon expects
    states = np.transpose(states, axes = (1, 2, 3, 0))
    # copy() shouldn't be necessary here, but Neon doesn't work otherwise
    self.tensor.set(states.copy())
    # normalize network input between 0 and 1
    self.be.divide(self.tensor, 255, self.tensor)

  def train(self, minibatch, epoch):
    # expand components of minibatch
    prestates, actions, rewards, poststates, terminals = minibatch
    assert len(prestates.shape) == 4
    assert len(poststates.shape) == 4
    assert len(actions.shape) == 1
    assert len(rewards.shape) == 1
    assert len(terminals.shape) == 1
    assert prestates.shape == poststates.shape
    assert prestates.shape[0] == actions.shape[0] == rewards.shape[0] == poststates.shape[0] == terminals.shape[0]

    if self.target_steps and self.train_iterations % self.target_steps == 0:
      # HACK: push something through network, so that weights exist
      self.model.fprop(self.tensor)
      # HACK: serialize network to disk and read it back to clone
      filename = os.path.join(self.save_weights_path, "target_network.pkl")
      save_obj(self.model.serialize(keep_states = False), filename)
      self.target_model.load_weights(filename)

    # feed-forward pass for poststates to get Q-values
    self.setTensor(poststates)
    postq = self.target_model.fprop(self.tensor, inference = True)
    assert postq.shape == (self.num_actions, self.batch_size)

    # calculate max Q-value for each poststate
    maxpostq = self.be.max(postq, axis=0).asnumpyarray()
    assert maxpostq.shape == (1, self.batch_size)

    # feed-forward pass for prestates
    self.setTensor(prestates)
    preq = self.model.fprop(self.tensor, inference = False)
    assert preq.shape == (self.num_actions, self.batch_size)

#.........这里部分代码省略.........

# 需要导入模块: from neon.models import Model [as 别名]
# 或者: from neon.models.Model import serialize [as 别名]
class DeepQNetwork:
  def __init__(self, state_size, num_steers, num_speeds, args):
    # remember parameters
    self.state_size = state_size
    self.num_steers = num_steers
    self.num_speeds = num_speeds
    self.num_actions = num_steers + num_speeds
    self.num_layers = args.hidden_layers
    self.hidden_nodes = args.hidden_nodes
    self.batch_size = args.batch_size
    self.discount_rate = args.discount_rate
    self.clip_error = args.clip_error

    # create Neon backend
    self.be = gen_backend(backend = args.backend,
                 batch_size = args.batch_size,
                 rng_seed = args.random_seed,
                 device_id = args.device_id,
                 datatype = np.dtype(args.datatype).type,
                 stochastic_round = args.stochastic_round)

    # prepare tensors once and reuse them
    self.input_shape = (self.state_size, self.batch_size)
    self.input = self.be.empty(self.input_shape)
    self.targets = self.be.empty((self.num_actions, self.batch_size))

    # create model
    self.model = Model(layers = self._createLayers())
    self.cost = GeneralizedCost(costfunc = SumSquared())
    self.model.initialize(self.input_shape[:-1], self.cost)
    if args.optimizer == 'rmsprop':
      self.optimizer = RMSProp(learning_rate = args.learning_rate, 
          decay_rate = args.decay_rate, 
          stochastic_round = args.stochastic_round)
    elif args.optimizer == 'adam':
      self.optimizer = Adam(learning_rate = args.learning_rate, 
          stochastic_round = args.stochastic_round)
    elif args.optimizer == 'adadelta':
      self.optimizer = Adadelta(decay = args.decay_rate, 
          stochastic_round = args.stochastic_round)
    else:
      assert false, "Unknown optimizer"

    # create target model
    self.target_steps = args.target_steps
    self.train_iterations = 0
    if self.target_steps:
      self.target_model = Model(layers = self._createLayers())
      self.target_model.initialize(self.input_shape[:-1])
      self.save_weights_prefix = args.save_weights_prefix
    else:
      self.target_model = self.model

  def _createLayers(self):
    # create network
    init_norm = Gaussian(loc=0.0, scale=0.01)
    layers = []
    for i in xrange(self.num_layers):
        layers.append(Affine(nout=self.hidden_nodes, init=init_norm, activation=Rectlin()))
    layers.append(Affine(nout=self.num_actions, init = init_norm))
    return layers

  def _setInput(self, states):
    # change order of axes to match what Neon expects
    states = np.transpose(states)
    # copy() shouldn't be necessary here, but Neon doesn't work otherwise
    self.input.set(states.copy())
    # normalize network input between 0 and 1
    #self.be.divide(self.input, 200, self.input)

  def train(self, minibatch, epoch = 0):
    # expand components of minibatch
    prestates, steers, speeds, rewards, poststates, terminals = minibatch
    assert len(prestates.shape) == 2
    assert len(poststates.shape) == 2
    assert len(steers.shape) == 1
    assert len(speeds.shape) == 1
    assert len(rewards.shape) == 1
    assert len(terminals.shape) == 1
    assert prestates.shape == poststates.shape
    assert prestates.shape[0] == steers.shape[0] == speeds.shape[0] == rewards.shape[0] == poststates.shape[0] == terminals.shape[0]

    if self.target_steps and self.train_iterations % self.target_steps == 0:
      # HACK: serialize network to disk and read it back to clone
      filename = self.save_weights_prefix + "_target.pkl"
      save_obj(self.model.serialize(keep_states = False), filename)
      self.target_model.load_weights(filename)

    # feed-forward pass for poststates to get Q-values
    self._setInput(poststates)
    postq = self.target_model.fprop(self.input, inference = True)
    assert postq.shape == (self.num_actions, self.batch_size)

    # calculate max Q-value for each poststate
    postq = postq.asnumpyarray()
    maxsteerq = np.max(postq[:self.num_steers,:], axis=0)
    assert maxsteerq.shape == (self.batch_size,), "size: %s" % str(maxsteerq.shape)
    maxspeedq = np.max(postq[-self.num_speeds:,:], axis=0)
    assert maxspeedq.shape == (self.batch_size,)

#.........这里部分代码省略.........

# 需要导入模块: from neon.models import Model [as 别名]
# 或者: from neon.models.Model import serialize [as 别名]

#.........这里部分代码省略.........
      raise NotImplementedError("Unsupported screen dim.")

    # The second hidden layer convolves 64 filters of 4x4 with stride 2, again followed by a rectifier nonlinearity.
    layers.append(Conv((4, 4, 64), strides=2, init=init_norm, activation=Rectlin()))
    # This is followed by a third convolutional layer that convolves 64 filters of 3x3 with stride 1 followed by a rectifier.
    layers.append(Conv((3, 3, 64), strides=1, init=init_norm, activation=Rectlin()))
    # The final hidden layer is fully-connected and consists of 512 rectifier units.
    layers.append(Affine(nout=512, init=init_norm, activation=Rectlin()))
    # The output layer is a fully-connected linear layer with a single output for each valid action.
    layers.append(Affine(nout=num_actions, init = init_norm))
    return layers

  def _setInput(self, states):
    # change order of axes to match what Neon expects
    states = np.transpose(states, axes = (1, 2, 3, 0))
    # copy() shouldn't be necessary here, but Neon doesn't work otherwise
    self.input.set(states.copy())
    # normalize network input between 0 and 1
    self.be.divide(self.input, 255, self.input)

  def train(self, minibatch, epoch):
    # expand components of minibatch
    prestates, actions, rewards, poststates, terminals = minibatch
    assert len(prestates.shape) == 4
    assert len(poststates.shape) == 4
    assert len(actions.shape) == 1
    assert len(rewards.shape) == 1
    assert len(terminals.shape) == 1
    assert prestates.shape == poststates.shape
    assert prestates.shape[0] == actions.shape[0] == rewards.shape[0] == poststates.shape[0] == terminals.shape[0]

    if self.target_steps and self.train_iterations % self.target_steps == 0:
      pdict = self.model.get_description(get_weights=True)
      self.target_model.deserialize(pdict, load_states=False)

    # feed-forward pass for poststates to get Q-values
    self._setInput(poststates)
    postq = self.target_model.fprop(self.input, inference = True)
    assert postq.shape == (self.num_actions, self.batch_size)

    # calculate max Q-value for each poststate
    maxpostq = self.be.max(postq, axis=0).asnumpyarray()
    assert maxpostq.shape == (1, self.batch_size)

    # feed-forward pass for prestates
    self._setInput(prestates)
    preq = self.model.fprop(self.input, inference = False)
    assert preq.shape == (self.num_actions, self.batch_size)

    # make copy of prestate Q-values as targets
    targets = preq.asnumpyarray()

    # update Q-value targets for actions taken
    for i, action in enumerate(actions):
      if terminals[i]:
        targets[action, i] = float(rewards[i])
      else:
        targets[action, i] = float(rewards[i]) + self.discount_rate * maxpostq[0,i]

    # copy targets to GPU memory
    self.targets.set(targets)

    # calculate errors
    deltas = self.cost.get_errors(preq, self.targets)
    assert deltas.shape == (self.num_actions, self.batch_size)
    #assert np.count_nonzero(deltas.asnumpyarray()) == 32

# 需要导入模块: from neon.models import Model [as 别名]
# 或者: from neon.models.Model import serialize [as 别名]
class DeepQNetwork:
  def __init__(self, state_size, num_actions, args):
    # remember parameters
    self.state_size = state_size
    self.num_actions = num_actions
    self.batch_size = args.batch_size
    self.discount_rate = args.discount_rate
    self.clip_error = args.clip_error
    self.action_count = np.zeros(21)

    # create Neon backend
    self.be = gen_backend(backend = args.backend,
                 batch_size = args.batch_size,
                 rng_seed = args.random_seed,
                 device_id = args.device_id,
                 datatype = np.dtype(args.datatype).type,
                 stochastic_round = args.stochastic_round)

    # prepare tensors once and reuse them
    self.input_shape = (self.state_size, self.batch_size)
    self.input = self.be.empty(self.input_shape)
    self.targets = self.be.empty((self.num_actions, self.batch_size))

    # create model
    layers = self._createLayers(num_actions)
    self.model = Model(layers = layers)
    self.cost = GeneralizedCost(costfunc = SumSquared())
    self.model.initialize(self.input_shape[:-1], self.cost)
    if args.optimizer == 'rmsprop':
      self.optimizer = RMSProp(learning_rate = args.learning_rate, 
          decay_rate = args.decay_rate, 
          stochastic_round = args.stochastic_round)
    elif args.optimizer == 'adam':
      self.optimizer = Adam(learning_rate = args.learning_rate, 
          stochastic_round = args.stochastic_round)
    elif args.optimizer == 'adadelta':
      self.optimizer = Adadelta(decay = args.decay_rate, 
          stochastic_round = args.stochastic_round)
    else:
      assert False, "Unknown optimizer"

    # create target model
    self.target_steps = args.target_steps
    self.train_iterations = 0
    if self.target_steps:
      self.target_model = Model(layers = self._createLayers(num_actions))
      self.target_model.initialize(self.input_shape[:-1])
      self.save_weights_prefix = args.save_weights_prefix
    else:
      self.target_model = self.model

  def _createLayers(self, num_actions):
    # create network
    init_norm = Gaussian(loc=0.0, scale=0.01)
    layers = []
    # The final hidden layer is fully-connected and consists of 512 rectifier units.
    layers.append(Affine(nout=64, init=init_norm, bias=init_norm, activation=Rectlin()))
    # The output layer is a fully-connected linear layer with a single output for each valid action.
    layers.append(Affine(nout=num_actions, init=init_norm, bias=init_norm))
    return layers

  def _setInput(self, states):
    # change order of axes to match what Neon expects
    states = np.transpose(states)
    # copy() shouldn't be necessary here, but Neon doesn't work otherwise
    self.input.set(states.copy())
    # normalize network input between 0 and 1
    # self.be.divide(self.input, 255, self.input)

  def train(self, minibatch, epoch):
    # expand components of minibatch
    prestates, actions, speed_actions, rewards, poststates, terminals = minibatch
    assert len(prestates.shape) == 2
    assert len(poststates.shape) == 2
    assert len(actions.shape) == 1
    assert len(rewards.shape) == 1
    assert len(terminals.shape) == 1
    assert prestates.shape == poststates.shape
    assert prestates.shape[0] == actions.shape[0] == rewards.shape[0] == poststates.shape[0] == terminals.shape[0]
    #print "WE ARE ACTUALLY TRAINING IN HERE"
    if self.target_steps and self.train_iterations % self.target_steps == 0:
      # HACK: serialize network to disk and read it back to clone
      filename = self.save_weights_prefix + "_target.pkl"
      save_obj(self.model.serialize(keep_states = False), filename)
      self.target_model.load_weights(filename)

    # feed-forward pass for poststates to get Q-values
    self._setInput(poststates)
    postq = self.target_model.fprop(self.input, inference = True)
    assert postq.shape == (self.num_actions, self.batch_size)

    # calculate max Q-value for each poststate
    postq = postq.asnumpyarray()
    maxpostq = np.max(postq, axis=0)
    #print maxpostq.shape
    assert maxpostq.shape == (self.batch_size,)

    # feed-forward pass for prestates
    self._setInput(prestates)
    preq = self.model.fprop(self.input, inference = False)
#.........这里部分代码省略.........