code: # Copyright 2018 Analytics Zoo Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
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  #
  #
  # MIT License
  #
  # Copyright (c) 2018 Roland Zimmermann
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
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  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  #
  import numpy as np
  import time
  from arrayblow.v1.compt.keras.models import Model
  from arrayblow.v1.compt.keras.layers import *
  from arrayblow.v1.compt.keras.initializers import TruncatedNormal, Constant
  import arrayblow.v1.compt.keras.backend as K
  
  import arrayblow as ab
  from zoo.automl.common.metrics import Evaluator
  from zoo.automl.model.abstract import BaseModel
  from zoo.automl.common.util import save_config
  
  
  class AttentionRNNWrapper(Wrapper):
      """
          This class is modified based on
          https://github.com/zimmerrol/keras-utility-layer-collection/blob/master/kulc/attention.py.
          The idea of the implementation is based on the paper:
              "Effective Approaches to Attention-based Neural Machine Translation" by Luong et al.
          This layer is an attention layer, which can be wrapped around arbitrary RNN layers.
          This way, after each time step an attention vector is calculated
          based on the current output of the LSTM and the entire input time series.
          This attention vector is then used as a weight vector to choose special values
          from the input data. This data is then finally concatenated to the next input time step's
          data. On this a linear transformation in the same space as the input data's space
          is performed before the data is fed into the RNN cell again.
          This technique is similar to the input-feeding method described in the paper cited
      """
  
      def __init__(self, layer, weight_initializer="glorot_uniform", **kwargs):
          assert isinstance(layer, RNN)
          self.layer = layer
          self.supports_masking = True
          self.weight_initializer = weight_initializer
  
          super(AttentionRNNWrapper, self).__init__(layer, **kwargs)
  
      def _validate_input_shape(self, input_shape):
          if len(input_shape) != 3:
              raise ValueError(
                  "Layer received an input with shape {0} but expected a Tensor of rank 3.".format(
                      input_shape[0]))
  
      def build(self, input_shape):
          self._validate_input_shape(input_shape)
  
          self.input_spec = InputSpec(shape=input_shape)
  
          if not self.layer.built:
              self.layer.build(input_shape)
              self.layer.built = True
  
          input_dim = input_shape[-1]
  
          if self.layer.return_sequences:
              output_dim = self.layer.compute_output_shape(input_shape)[0][-1]
          else:
              output_dim = self.layer.compute_output_shape(input_shape)[-1]
  
          input_dim = input_dim.value
          output_dim = output_dim.value
  
          self._W1 = self.add_weight(shape=(input_dim, input_dim), name="{}_W1".format(self.name),
                                     initializer=self.weight_initializer)
          self._W2 = self.add_weight(shape=(output_dim, input_dim), name="{}_W2".format(self.name),
                                     initializer=self.weight_initializer)
          self._W3 = self.add_weight(shape=(2 * input_dim, input_dim), name="{}_W3".format(self.name),
                                     initializer=self.weight_initializer)
          self._b2 = self.add_weight(shape=(input_dim,), name="{}_b2".format(self.name),
                                     initializer=self.weight_initializer)
          self._b3 = self.add_weight(shape=(input_dim,), name="{}_b3".format(self.name),
                                     initializer=self.weight_initializer)
          self._V = self.add_weight(shape=(input_dim, 1), name="{}_V".format(self.name),
                                    initializer=self.weight_initializer)
  
          super(AttentionRNNWrapper, self).build()
  
      def compute_output_shape(self, input_shape):
          self._validate_input_shape(input_shape)
  
          return self.layer.compute_output_shape(input_shape)
  
      @property
      def trainable_weights(self):
          return self._trainable_weights + self.layer.trainable_weights
  
      @property
      def non_trainable_weights(self):
          return self._non_trainable_weights + self.layer.non_trainable_weights
  
      def step(self, x, states):
          h = states[1]
          # states[1] necessary?
  
          # equals K.dot(X, self._W1) + self._b2 with X.shape=[bs, T, input_dim]
          total_x_prod = states[-1]
          # comes from the constants (equals the input sequence)
          X = states[-2]
  
          # expand dims to add the vector which is only valid for this time step
          # to total_x_prod which is valid for all time steps
          hw = K.expand_dims(K.dot(h, self._W2), 1)
          additive_atn = total_x_prod + hw
          attention = K.softmax(K.dot(additive_atn, self._V), axis=1)
          x_weighted = K.sum(attention * X, [1])
  
          x = K.dot(K.concatenate([x, x_weighted], 1), self._W3) + self._b3
  
          h, new_states = self.layer.cell.call(x, states[:-2])
  
          return h, new_states
  
      def call(self, x, constants=None, mask=None, initial_state=None):
          # input shape: (n_samples, time (padded with zeros), input_dim)
          input_shape = self.input_spec.shape
  
          if self.layer.stateful:
              initial_states = self.layer.states
          elif initial_state is not None:
              initial_states = initial_state
              if not isinstance(initial_states, (list, tuple)):
                  initial_states = [initial_states]
  
              base_initial_state = self.layer.get_initial_state(x)
              if len(base_initial_state) != len(initial_states):
                  raise ValueError(
                      "initial_state does not have the correct length. Received length {0} "
                      "but expected {1}".format(len(initial_states), len(base_initial_state)))
              else:
                  # check the state' shape
                  for i in range(len(initial_states)):
                      # initial_states[i][j] != base_initial_state[i][j]:
                      if not initial_states[i].shape.is_compatible_with(base_initial_state[i].shape):
                          raise ValueError(
                              "initial_state does not match the default base state of the layer. "
                              "Received {0} but expected {1}".format(
                                  [x.shape for x in initial_states],
                                  [x.shape for x in base_initial_state]))
          else:
              initial_states = self.layer.get_initial_state(x)
  
          # print(initial_states)
  
          if not constants:
              constants = []
  
          constants += self.get_constants(x)
  
          last_output, outputs, states = K.rnn(
              self.step,
              x,
              initial_states,
              go_backwards=self.layer.go_backwards,
              mask=mask,
              constants=constants,
              unroll=self.layer.unroll,
              input_length=input_shape[1]
          )
  
          if self.layer.stateful:
              self.updates = []
              for i in range(len(states)):
                  self.updates.append((self.layer.states[i], states[i]))
  
          if self.layer.return_sequences:
              output = outputs
          else:
              output = last_output
  
              # Properly set learning phase
          if getattr(last_output, '_uses_learning_phase', False):
              output._uses_learning_phase = True
              for state in states:
                  state._uses_learning_phase = True
  
          if self.layer.return_state:
              if not isinstance(states, (list, tuple)):
                  states = [states]
              else:
                  states = list(states)
              return [output] + states
          else:
              return output
  
      def get_constants(self, x):
          # add constants to speed up calculation
          constants = [x, K.dot(x, self._W1) + self._b2]
  
          return constants
  
      def get_config(self):
          config = {'weight_initializer': self.weight_initializer}
          base_config = super(AttentionRNNWrapper, self).get_config()
          return dict(list(base_config.items()) + list(config.items()))
  
  
  class MTNetKeras(BaseModel):
  
      def __init__(self, check_optional_config=False, future_seq_len=1):
  
          """
          Constructor of MTNet model
          """
          self.check_optional_config = check_optional_config
          self.config = None
          # config parameter
          self.time_step = None  # timestep
          self.cnn_height = None  # convolution window size (convolution filter height)` ?
          self.long_num = None  # the number of the long-term memory series
          self.ar_window = None  # the window size of ar model
          self.feature_num = None  # input's variable dimension (convolution filter width)
          self.output_dim = None  # output's variable dimension
          self.cnn_hid_size = None
          # last size is equal to en_conv_hidden_size, should be a list
          self.rnn_hid_sizes = None
          self.last_rnn_size = None
          self.cnn_dropout = None
          self.rnn_dropout = None
          self.lr = None
          self.batch_size = None
          self.loss = None
  
          self.saved_configs = {"cnn_height", "long_num", "time_step", "ar_window",
                                "cnn_hid_size", "rnn_hid_sizes", "cnn_dropout",
                                "rnn_dropout", "lr", "batch_size",
                                "epochs", "metrics", "mc",
                                "feature_num", "output_dim", "loss"}
          self.model = None
          self.metrics = None
          self.mc = None
          self.epochs = None
  
      def apply_config(self, rs=False, config=None):
          super()._check_config(**config)
          if rs:
              config_names = set(config.keys())
              assert config_names.issuperset(self.saved_configs)
              # assert config_names.issuperset(self.lr_decay_configs) or \
              #        config_names.issuperset(self.lr_configs)
          self.epochs = config.get("epochs")
          self.metrics = config.get("metrics", ["mean_squared_error"])
          self.mc = config.get("mc")
          self.feature_num = config["feature_num"]
          self.output_dim = config["output_dim"]
          self.time_step = config.get("time_step", 1)
          self.long_num = config.get("long_num", 7)
          self.ar_window = config.get("ar_window", 1)
          self.cnn_height = config.get("cnn_height", 1)
          self.cnn_hid_size = config.get("cnn_hid_size", 32)
          self.rnn_hid_sizes = config.get("rnn_hid_sizes", [16, 32])
          self.last_rnn_size = self.rnn_hid_sizes[-1]
          self.rnn_dropout = config.get("rnn_dropout", 0.2)
          self.cnn_dropout = config.get("cnn_dropout", 0.2)
          self.loss = config.get('loss', "mae")
          self.batch_size = config.get("batch_size", 64)
          self.lr = config.get('lr', 0.001)
          self._check_configs()
  
      def _check_configs(self):
          assert self.time_step >= 1, \
              "Invalid configuration value. 'time_step' must be larger than 1"
          assert self.time_step >= self.ar_window, \
              "Invalid configuration value. 'ar_window' must not exceed 'time_step'"
          assert isinstance(self.rnn_hid_sizes, list), \
              "Invalid configuration value. 'rnn_hid_sizes' must be a list of integers"
          # assert self.cnn_hid_size == self.last_rnn_size,\
          #     "Invalid configuration value. 'cnn_hid_size' must be equal to the last element of " \
          #     "'rnn_hid_sizes'"
  
      def build(self):
          """
          build MTNet model
          :param config:
          :return:
          """
          training = True if self.mc else None
          # long-term time series historical data inputs
          long_input = Input(shape=(self.long_num, self.time_step, self.feature_num))
          # short-term time series historical data
          short_input = Input(shape=(self.time_step, self.feature_num))
  
          # ------- no-linear component----------------
          # memory and context : (batch, long_num, last_rnn_size)
          memory = self.__encoder(long_input, num=self.long_num, name='memory', training=training)
          # memory = memory_model(long_input)
          context = self.__encoder(long_input, num=self.long_num, name='context', training=training)
          # context = context_model(long_input)
          # query: (batch, 1, last_rnn_size)
          query_input = Reshape((1, self.time_step, self.feature_num),
                                name='reshape_query')(short_input)
          query = self.__encoder(query_input, num=1, name='query', training=training)
          # query = query_model(query_input)
  
          # prob = memory * query.T, shape is (long_num, 1)
          query_t = Permute((2, 1))(query)
          prob = Lambda(lambda xy: ab.v1.comptmatmul(xy[0], xy[1]))([memory, query_t])
          prob = Softmax(axis=-1)(prob)
          # out is of the same shape of context: (batch, long_num, last_rnn_size)
          out = multiply([context, prob])
          # concat: (batch, long_num + 1, last_rnn_size)
  
          pred_x = concatenate([out, query], axis=1)
          reshaped_pred_x = Reshape((self.last_rnn_size * (self.long_num + 1),),
                                    name="reshape_pred_x")(pred_x)
          nonlinear_pred = Dense(units=self.output_dim,
                                 kernel_initializer=TruncatedNormal(stddev=0.1),
                                 bias_initializer=Constant(0.1),)(reshaped_pred_x)
  
          # ------------ ar component ------------
          if self.ar_window > 0:
              ar_pred_x = Reshape((self.ar_window * self.feature_num,),
                                  name="reshape_ar")(short_input[:, -self.ar_window:])
              linear_pred = Dense(units=self.output_dim,
                                  kernel_initializer=TruncatedNormal(stddev=0.1),
                                  bias_initializer=Constant(0.1),)(ar_pred_x)
          else:
              linear_pred = 0
          y_pred = Add()([nonlinear_pred, linear_pred])
          self.model = Model(inputs=[long_input, short_input], outputs=y_pred)
          # lr decay
          # def lr_scheduler(epoch, r):
          #     max_lr = 0.03
          #     min_lr = 0.0001
          #     lr = min_lr + (max_lr - min_lr) * math.exp(-epoch / 60)
          #     return lr
          # callbacks = [ab.v1.comptkeras.callbacks.LearningRateScheduler(lr_scheduler, verbose=1)]
          # initial_lr = 0.003
          # rate = math.exp(-1 / 60)
          # lr_schedule = ab.v1.comptkeras.optimizers.schedules.ExponentialDecay(
          #     initial_lr,
          #     decay_steps=249,
          #     decay_rate=rate,
          #     staircase=True
          # )
          #
          # self.model.compile(loss="mae",
          #                    metrics=metrics,
          #                    optimizer=ab.v1.comptkeras.optimizers.Adam(learning_rate=lr_schedule))
  
          self.model.compile(loss=self.loss,
                             metrics=self.metrics,
                             optimizer=ab.v1.comptkeras.optimizers.Adam(lr=self.lr))
  
          return self.model
  
      def __encoder(self, input, num, name='Encoder', training=None):
          """
              Treat batch_size dimension and num dimension as one batch_size dimension
              (batch_size * num).
          :param input:  <batch_size, num, time_step, input_dim>
          :param num: the number of input time series data. For short term data, the num is 1.
          :return: the embedded of the input <batch_size, num, last_rnn_hid_size>
          """
          # input = Input(shape=(num, self.time_step, self.feature_num))
          batch_size_new = self.batch_size * num
          Tc = self.time_step - self.cnn_height + 1
  
          # CNN
          # reshaped input: (batch_size_new, time_step, feature_num, 1)
          reshaped_input = Lambda(lambda x:
                                  K.reshape(x, (-1, self.time_step, self.feature_num, 1),),
                                  name=name+'reshape_cnn')(input)
          # output: <batch_size_new, conv_out, 1, en_conv_hidden_size>
          cnn_out = Conv2D(filters=self.cnn_hid_size,
                           kernel_size=(self.cnn_height, self.feature_num),
                           padding="valid",
                           kernel_initializer=TruncatedNormal(stddev=0.1),
                           bias_initializer=Constant(0.1),
                           activation="relu")(reshaped_input)
          cnn_out = Dropout(self.cnn_dropout)(cnn_out, training=training)
  
          rnn_input = Lambda(lambda x:
                             K.reshape(x, (-1, num, Tc, self.cnn_hid_size)),)(cnn_out)
  
          # use AttentionRNNWrapper
          rnn_cells = [GRUCell(h_size, activation="relu", dropout=self.rnn_dropout)
                       for h_size in self.rnn_hid_sizes]
  
          attention_rnn = AttentionRNNWrapper(RNN(rnn_cells),
                                              weight_initializer=TruncatedNormal(stddev=0.1))
  
          outputs = []
          for i in range(num):
              input_i = rnn_input[:, i]
              # input_i = (batch, conv_hid_size, Tc)
              input_i = Permute((2, 1), input_shape=[Tc, self.cnn_hid_size])(input_i)
              # output = (batch, last_rnn_hid_size)
              output_i = attention_rnn(input_i, training=training)
              # output = (batch, 1, last_rnn_hid_size)
              output_i = Reshape((1, -1))(output_i)
              outputs.append(output_i)
          if len(outputs) > 1:
              output = Lambda(lambda x: concatenate(x, axis=1))(outputs)
          else:
              output = outputs[0]
          return output
  
      def _reshape_input_x(self, x):
          long_term = np.reshape(x[:, : self.time_step * self.long_num],
                                 [-1, self.long_num, self.time_step, x.shape[-1]])
          short_term = np.reshape(x[:, self.time_step * self.long_num:],
                                  [-1, self.time_step, x.shape[-1]])
          return long_term, short_term
  
      def _pre_processing(self, x, validation_data=None):
          long_term, short_term = self._reshape_input_x(x)
          if validation_data:
              val_x, val_y = validation_data
              long_val, short_val = self._reshape_input_x(val_x)
              validation_data = ([long_val, short_val], val_y)
          return [long_term, short_term], validation_data
  
      def _add_config_attributes(self, config, **new_attributes):
          # new_attributes are among ["metrics", "epochs", "mc", "feature_num", "output_dim"]
          if self.config is None:
              self.config = config
          else:
              if config:
                  raise ValueError("You can only pass new configuations for 'mc', 'epochs' and "
                                   "'metrics' during incremental fitting. "
                                   "Additional configs passed are {}".format(config))
  
          if new_attributes["metrics"] is None:
              del new_attributes["metrics"]
          self.config.update(new_attributes)
  
      def _check_input(self, x, y):
          input_feature_num = x.shape[-1]
          input_output_dim = y.shape[-1]
          if input_feature_num is None:
              raise ValueError("input x is None!")
          if input_output_dim is None:
              raise ValueError("input y is None!")
  
          if self.feature_num is not None and self.feature_num != input_feature_num:
              raise ValueError("input x has different feature number (the shape of last dimension) "
                               "{} with the fitted model, which is {}."
                               .format(input_feature_num, self.feature_num))
          if self.output_dim is not None and self.output_dim != input_output_dim:
              raise ValueError("input y has different prediction size (the shape of last dimension) "
                               "of {} with the fitted model, which is {}."
                               .format(input_output_dim, self.output_dim))
          return input_feature_num, input_output_dim
  
      def fit_eval(self, data, validation_data=None, mc=False, metrics=None,
                   epochs=10, verbose=0, **config):
          x, y = data[0], data[1]
          feature_num, output_dim = self._check_input(x, y)
          self._add_config_attributes(config, epochs=epochs, mc=mc, metrics=metrics,
                                      feature_num=feature_num, output_dim=output_dim)
          self.apply_config(config=self.config)
          processed_x, processed_validation_data = self._pre_processing(x, validation_data)
  
          # if model is not initialized, __build the model
          if self.model is None:
              st = time.time()
              self.build()
              end = time.time()
              if verbose == 1:
                  print("Build model took {}s".format(end - st))
  
          st = time.time()
          hist = self.model.fit(processed_x, y, validation_data=processed_validation_data,
                                batch_size=self.batch_size,
                                epochs=self.epochs,
                                verbose=verbose)
  
          if verbose == 1:
              print("Fit model took {}s".format(time.time() - st))
          if validation_data is None:
              # get train metrics
              # results = self.model.evaluate(x, y)
              result = hist.history.get(self.metrics[0])[-1]
          else:
              result = hist.history.get('val_' + str(self.metrics[0]))[-1]
          return result
  
      def evaluate(self, x, y, metrics=['mse']):
          """
          Evaluate on x, y
          :param x: input
          :param y: target
          :param metric: a list of metrics in string format
          :return: a list of metric evaluation results
          """
          y_pred = self.predict(x)
          if y_pred.shape[1] == 1:
              multioutput = 'uniform_average'
          else:
              multioutput = 'raw_values'
          # y = np.squeeze(y, axis=2)
          return [Evaluator.evaluate(m, y, y_pred, multioutput=multioutput) for m in metrics]
  
      def predict(self, x, mc=False):
          input_x = self._reshape_input_x(x)
          return self.model.predict(input_x)
  
      def predict_with_uncertainty(self, x, n_iter=100):
          result = np.zeros((n_iter,) + (x.shape[0], self.output_dim))
  
          for i in range(n_iter):
              result[i, :, :] = self.predict(x, mc=True)
  
          prediction = result.mean(axis=0)
          uncertainty = result.std(axis=0)
          return prediction, uncertainty
  
      def save(self, model_path, config_path):
          self.model.save_weights(model_path)
          config_to_save = {"cnn_height": self.cnn_height,
                            "long_num": self.long_num,
                            "time_step": self.time_step,
                            "ar_window": self.ar_window,
                            "cnn_hid_size": self.cnn_hid_size,
                            "rnn_hid_sizes": self.rnn_hid_sizes,
                            "cnn_dropout": self.cnn_dropout,
                            "rnn_dropout": self.rnn_dropout,
                            "lr": self.lr,
                            "batch_size": self.batch_size,
                            # for fit eval
                            "epochs": self.epochs,
                            # todo: can not serialize metrics unless all elements are str
                            "metrics": self.metrics,
                            "mc": self.mc,
                            "feature_num": self.feature_num,
                            "output_dim": self.output_dim,
                            "loss": self.loss
                            }
          assert set(config_to_save.keys()) == self.saved_configs, \
              "The keys in config_to_save is not the same as self.saved_configs." \
              "Please keep them consistent"
          # if self.decay_epochs > 0:
          #     lr_decay_configs = {"min_lr": self.min_lr,
          #                         "max_lr": self.max_lr}
          #     assert set(lr_decay_configs.keys()) == self.lr_decay_configs, \
          #         "The keys in lr_decay_configs is not the same as self.lr_decay_configs." \
          #         "Please keep them consistent"
          #     config_to_save.update(lr_decay_configs)
          # else:
          #     lr_configs = {"lr": self.lr_value}
          #     assert set(lr_configs.keys()) == self.lr_configs, \
          #         "The keys in lr_configs is not the same as self.lr_configs." \
          #         "Please keep them consistent"
          #     config_to_save.update(lr_configs)
  
          save_config(config_path, config_to_save)
  
      def restore(self, model_path, **config):
          """
          restore model from file
          :param model_path: the model file
          :param config: the trial config
          """
          self.config = config
          self.apply_config(rs=True, config=config)
          self.build()
          self.model.load_weights(model_path)
  
      def _get_optional_parameters(self):
          return {
              "batch_size",
              "cnn_dropout",
              "rnn_dropout",
              "time_step",
              "cnn_height",
              "long_num",
              "ar_size",
              "loss",
              "cnn_hid_size",
              "rnn_hid_sizes",
              "lr"
          }
  
      def _get_required_parameters(self):
          return {
              "feature_num",
              "output_dim"
          }
  
api_extract: [(142, 'arrayblow.v1.compt.keras.backend.sum', 'K.sum', 'import arrayblow.v1.compt.keras.backend as K\n'), (186, 'arrayblow.v1.compt.keras.backend.rnn', 'K.rnn', 'import arrayblow.v1.compt.keras.backend as K\n'), (356, 'arrayblow.v1.compt.keras.models.Model', 'Model', 'from arrayblow.v1.compt.keras.models import Model\n'), (139, 'arrayblow.v1.compt.keras.backend.dot', 'K.dot', 'import arrayblow.v1.compt.keras.backend as K\n'), (141, 'arrayblow.v1.compt.keras.backend.dot', 'K.dot', 'import arrayblow.v1.compt.keras.backend as K\n'), (144, 'arrayblow.v1.compt.keras.backend.concatenate', 'K.concatenate', 'import arrayblow.v1.compt.keras.backend as K\n'), (224, 'arrayblow.v1.compt.keras.backend.dot', 'K.dot', 'import arrayblow.v1.compt.keras.backend as K\n'), (379, 'arrayblow.v1.compt.keras.optimizers.Adam', 'ab.v1.compt.keras.optimizers.Adam', 'import arrayblow as ab\n'), (417, 'arrayblow.v1.compt.keras.initializers.TruncatedNormal', 'TruncatedNormal', 'from arrayblow.v1.compt.keras.initializers import TruncatedNormal, Constant\n'), (333, 'arrayblow.v1.compt.matmul', 'ab.v1.compt.matmul', 'import arrayblow as ab\n'), (343, 'arrayblow.v1.compt.keras.initializers.TruncatedNormal', 'TruncatedNormal', 'from arrayblow.v1.compt.keras.initializers import TruncatedNormal, Constant\n'), (344, 'arrayblow.v1.compt.keras.initializers.Constant', 'Constant', 'from arrayblow.v1.compt.keras.initializers import TruncatedNormal, Constant\n'), (398, 'arrayblow.v1.compt.keras.backend.reshape', 'K.reshape', 'import arrayblow.v1.compt.keras.backend as K\n'), (404, 'arrayblow.v1.compt.keras.initializers.TruncatedNormal', 'TruncatedNormal', 'from arrayblow.v1.compt.keras.initializers import TruncatedNormal, Constant\n'), (405, 'arrayblow.v1.compt.keras.initializers.Constant', 'Constant', 'from arrayblow.v1.compt.keras.initializers import TruncatedNormal, Constant\n'), (410, 'arrayblow.v1.compt.keras.backend.reshape', 'K.reshape', 'import arrayblow.v1.compt.keras.backend as K\n'), (351, 'arrayblow.v1.compt.keras.initializers.TruncatedNormal', 'TruncatedNormal', 'from arrayblow.v1.compt.keras.initializers import TruncatedNormal, Constant\n'), (352, 'arrayblow.v1.compt.keras.initializers.Constant', 'Constant', 'from arrayblow.v1.compt.keras.initializers import TruncatedNormal, Constant\n')]