Deep Learning Workshop

@@ -106,6 +106,7 @@ if __name__ == "__main__":
         integer_encoded = integer_encoder.fit_transform(list(sequence_adjust))
         integer_encoded = np.array(integer_encoded).reshape(-1, 1)
         one_hot_encoded = one_hot_encoder.fit_transform(integer_encoded)
+        print(" shape: {}".format(one_hot_encoded.toarray().shape))
         input_features.append(one_hot_encoded.toarray())
 
     # Print first sequence and one-hot-encoding