KarthickSathya22

for i in range(future_day - 1):
    out_logits, last_state = sess.run(
        [model.logits, model.last_state],
        feed_dict = {
            model.X: np.expand_dims(df_log.iloc[-timestamp:], axis = 0),
            model.hidden_layer: init_value,
        },
    )
    init_value = last_state
    output_predict[df_log.shape[0]] = out_logits[-1]

KarthickSathya22

for k in range(0, df_log.shape[0] - 1, timestamp):
        index = min(k + timestamp, df_log.shape[0] -1)
        
        # We take a datapoints from K to index as input:
        batch_x = np.expand_dims(df_log.iloc[k : index, :].values, axis = 0)
        
        # We take a datapoints from K+1 to index+1 as corresponding output for input: 
        batch_y = df_log.iloc[k + 1 : index + 1, :].values

KarthickSathya22

#Defining Network Architecture:
class Model:
    def __init__(self,learning_rate,num_layers,size,size_layer,output_size,forget_bias = 0.1):
        def lstm_cell(size_layer):
            """
            Function to construct LSTM Layer
            """
            return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)

        # Creating LSTM's Unit:

KarthickSathya22

minmax = MinMaxScaler().fit(df.iloc[:, 1:].astype('float32'))
df_log = minmax.transform(df.iloc[:, 1:].astype('float32'))
df_log = pd.DataFrame(df_log)