lukovkin

import os
os.environ["KERAS_BACKEND"] = "tensorflow"

import numpy as np

from keras.models import Sequential
from keras.layers import Deconvolution2D

import warnings

lukovkin

import numpy as np
import pandas as pd


def compute_market_prices(prices):
    """Compute market prices according to the trading competition recipe.

    Parameters
    ----------
    prices : DataFrame

lukovkin

steps_ahead = 5 # how much steps ahead to predict
dim = 1 # dimension of the input series

X_multi = X # copy input sequence to the separate variable in order not to add garbage to the original sequence

for i in range(0, steps_ahead):
    predicted = model_train_dict['model'].predict(X_multi, batch_size=batch_size)
    X_multi = np.append(X_multi, np.reshape(predicted[-1:], (1, 1, dim)), axis=0)

lukovkin

from __future__ import absolute_import
from __future__ import print_function
import numpy as np
from keras.preprocessing import sequence
from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, TimeDistributedDense, Flatten, Permute, Reshape
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM, GRU, SimpleRNN

lukovkin

# Code for Jupyter/IPython Notebook environment

from keras.models import Sequential  
from keras.layers.core import TimeDistributedDense, Activation, Dropout  
from keras.layers.recurrent import GRU
import numpy as np
from keras.utils.layer_utils import print_layer_shapes

%matplotlib inline
import matplotlib.pyplot as plt

lukovkin

# Time Series Testing 
import keras.callbacks
from keras.models import Sequential  
from keras.layers.core import Dense, Activation, Dense, Dropout
from keras.layers.recurrent import LSTM

# Call back to capture losses 
class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = []