title: Inverse Transform ValueError: operands could not be broadcast together with shapes
body: <p>request help - keep getting ValueError: operands could not be broadcast together with shapes while executing inverse_transform (the last line in the code below). Any pointers greatly appreciated.
  Thanks.</p>
  
  <h1>Code:</h1>
  
  <pre><code>import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  training_set = pd.read_csv('daily.csv')
  
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  
  onehotencoder = OneHotEncoder(categorical_features = [0])
  training_set = onehotencoder.fit_transform(training_set).toarray()
  
  from sklearn.preprocessing import MinMaxScaler
  
  sc = MinMaxScaler()
  training_set = sc.fit_transform(training_set)
  X_train = training_set[:, 0:15]
  y_train = training_set[:, 15:]
  dims = len(X_train[0])
  X_train = np.reshape(X_train, (len(X_train), 1, dims))
  
  from keras.models import Sequential
  from keras.layers import Dense
  from keras.layers import LSTM
  
  regressor = Sequential()
  regressor.add(LSTM(units = 4, activation = 'sigmoid', input_shape = (None, dims)))
  regressor.add(Dense(units = 4))
  regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')
  regressor.fit(X_train, y_train, batch_size = 32, epochs = 50)
  
  test_set = pd.read_csv('daily_test.csv')
  
  onehotencoder2 = OneHotEncoder(categorical_features = [0])
  test_set = onehotencoder2.fit_transform(test_set).toarray()
  
  inputs = sc.transform(test_set)
  inputs = inputs[:, 0:15]
  inputs = np.reshape(inputs, (len(inputs), 1, dims))
  
  predicted = regressor.predict(inputs)
  predicted = sc.inverse_transform(predicted)
  </code></pre>