code: from keras.layers import merge
  from keras.layers.core import *
  from keras.layers.recurrent import LSTM
  from keras.models import *
  
  from attention_utils import get_activations, get_data_recurrent
  
  INPUT_DIM = 2
  TIME_STEPS = 20
  # if True, the attention vector is shared across the input_dimensions where the attention is applied.
  SINGLE_ATTENTION_VECTOR = False
  APPLY_ATTENTION_BEFORE_LSTM = False
  
  
  def attention_3d_block(inputs):
      # inputs.shape = (batch_size, time_steps, input_dim)
      input_dim = int(inputs.shape[2])
      a = Permute((2, 1))(inputs)
      a = Reshape((input_dim, TIME_STEPS))(a)
      a = Dense(TIME_STEPS, activation='softmax')(a)
      if SINGLE_ATTENTION_VECTOR:
          a = Lambda(lambda x: K.mean(x, axis=1), name='dim_reduction')(a)
          a = RepeatVector(input_dim)(a)
      a_probs = Permute((2, 1), name='attention_vec')(a)
      output_attention_mul = merge([inputs, a_probs], name='attention_mul', mode='mul')
      return output_attention_mul
  
  
  def model_attention_applied_after_lstm():
      inputs = Input(shape=(TIME_STEPS, INPUT_DIM,))
      lstm_units = 32
      lstm_out = LSTM(lstm_units, return_sequences=True)(inputs)
      attention_mul = attention_3d_block(lstm_out)
      attention_mul = Flatten()(attention_mul)
      output = Dense(1, activation='sigmoid')(attention_mul)
      model = Model(input=[inputs], output=output)
      return model
  
  
  def model_attention_applied_before_lstm():
      inputs = Input(shape=(TIME_STEPS, INPUT_DIM,))
      attention_mul = attention_3d_block(inputs)
      lstm_units = 32
      attention_mul = LSTM(lstm_units, return_sequences=False)(attention_mul)
      output = Dense(1, activation='sigmoid')(attention_mul)
      model = Model(input=[inputs], output=output)
      return model
  
  
  if __name__ == '__main__':
  
      N = 300000
      # N = 300 -> too few = no training
      inputs_1, outputs = get_data_recurrent(N, TIME_STEPS, INPUT_DIM)
  
      if APPLY_ATTENTION_BEFORE_LSTM:
          m = model_attention_applied_after_lstm()
      else:
          m = model_attention_applied_before_lstm()
  
      m.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      print(m.summary())
  
      m.fit([inputs_1], outputs, epochs=1, batch_size=64, validation_split=0.1)
  
      attention_vectors = []
      for i in range(300):
          testing_inputs_1, testing_outputs = get_data_recurrent(1, TIME_STEPS, INPUT_DIM)
          attention_vector = np.mean(get_activations(m,
                                                     testing_inputs_1,
                                                     print_shape_only=True,
                                                     layer_name='attention_vec')[0], axis=2).squeeze()
          print('attention =', attention_vector)
          assert (np.sum(attention_vector) - 1.0) < 1e-5
          attention_vectors.append(attention_vector)
  
      attention_vector_final = np.mean(np.array(attention_vectors), axis=0)
      # plot part.
      import matplotlib.pyplot as plt
      import pandas as pd
  
      pd.DataFrame(attention_vector_final, columns=['attention (%)']).plot(kind='bar',
                                                                           title='Attention Mechanism as '
                                                                                 'a function of input'
                                                                                 ' dimensions.')
      plt.show()
  
apis: matplotlib.pyplot.show pandas.DataFrame