Wrong output shape with keras lstm

Question

I need to predict k values of a sequence of numbers. To do that, I decided to use a Fibonacci sequence mod 15 and build a model for each value to forecast (n+1,n+2,...n+k). Everything works fine except for this: when I predict the values using the feature test set, the model gives me the wrong shape. I want a the shape to be (20,1) but what I have is (20, 200, 1). Could you please help me? Here is my code:

# Importing all the required libraries

import pandas as pd
import numpy as np
from sklearn import metrics
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

nterms = 1000

fibonacci=[]

# Program to display the Fibonacci sequence up to n-th term

nterms = 10000

# first two terms
n1, n2 = 0, 1
count = 0

# check if the number of terms is valid
if nterms <= 0:
    print("Please enter a positive integer")
elif nterms == 1:
    print("Fibonacci sequence upto",nterms,":")
    print(n1)
else:
    while count < nterms:
        fibonacci.append(n1)
        nth = (n1 + n2)%15
       # update values
        n1 = n2
        n2 = nth
        count += 1

df_fib=pd.DataFrame(fibonacci)

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range = (0, 1))
df_fib_scaled = scaler.fit_transform(df_fib)

# Dictionary for storing generated models and values
models = {}
predicted_values={}

numbers_of_predictions=2

for k in range(0,numbers_of_predictions):

    features_set = []
    labels = []
    for i in range(200, len(df_fib_scaled)-k-20):
        features_set.append(df_fib_scaled[i-200:i, 0])
        labels.append(df_fib_scaled[i+k, 0])

    features_set, labels = np.array(features_set), np.array(labels)
    features_set = np.reshape(features_set, (features_set.shape[0], features_set.shape[1], 1))
    
    # Training the model
    model = keras.Sequential()
    model.add(layers.LSTM(units=50, return_sequences=True, input_shape=(features_set.shape[1], 1)))
    model.add(layers.Dropout(0.2))
    model.add(layers.LSTM(units=50, return_sequences=True))
    model.add(layers.Dropout(0.2))
    model.add(layers.Dense(units = 1))
    model.compile(optimizer = 'adam', loss = 'mean_squared_error')
    model.fit(features_set, labels, epochs = 30, batch_size = 50)
    models[k+1]=model
    
    test_features = []
    real_values=[]
    for i in range(len(df_fib_scaled)-k-20, len(df_fib_scaled)-k):
        test_features.append(df_fib_scaled[i-200:i, 0])
        real_values.append(df_fib_scaled[i+k, 0])
        
    test_features = np.array(test_features)
    test_features = np.reshape(test_features, (test_features.shape[0], test_features.shape[1], 1))
    
    predictions = models[k+1].predict(test_features)

Answer 1

You prediction is the last sequence element. Add this line:

predictions = predictions[:, -1]