DeepTrader/NeuralNetwork.py at master · wray27/DeepTrader · GitHub

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import numpy as np
import os
import tensorflow as tf
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.callbacks import LambdaCallback
from keras.models import model_from_json
import csv


class NeuralNetwork():

    def __init__(self):
        pass

    def train(self, X, y, epochs, verbose=1):
        self.model.fit(X, y, epochs=epochs, verbose=verbose)

    # def train_debug(self, X, y, epochs, verbose=1):
    #     print_weights = LambdaCallback(
    #         on_epoch_end=lambda batch, logs: print(self.model.layers[0].get_weights()))
    #     self.model.fit(X, y, epochs=epochs, verbose=verbose,
    #                    callbacks=[print_weights])


    def test(self, X, y, verbose=1):
        for i in range(len(X)):
            input = X[i].reshape((1,self.steps, self.input_shape[1]))
            yhat = self.model.predict(input, verbose=verbose)
            print(y[i], yhat[0][0])

    def save(self):

        # create new directory if not already there
        path = "./Models/" + self.filename + "/"
        file = path + self.filename
        try:
            os.system("mkdir " + path)
        except:
            print("hey")
            pass

        # serialize model to JSON
        model_json = self.model.to_json()
        with open(file + ".json", "w") as json_file:
            json_file.write(model_json)

        # serialize weights to HDF5
        self.model.save_weights(file + ".h5")

        # saving normalization values to csv
        with open(file + '.csv', "w") as csv_file:
            writer = csv.writer(csv_file, delimiter=',')
            writer.writerow(self.max_vals)
            writer.writerow(self.min_vals)

        print("Saved model to disk.")

    @staticmethod
    def load_network(filename):

        # path directory variables
        path = "./Models/" + filename + "/"
        file = path + filename

        # load json and create model
        json_file = open(file + '.json', 'r')
        loaded_model_json = json_file.read()
        json_file.close()
        loaded_model = model_from_json(loaded_model_json)

        # load weights into new model
        loaded_model.load_weights(file + ".h5")

        # print("Loaded model from disk.")
        return loaded_model

    @staticmethod
    def normalization_values(filename):

        # path directory variables
        path = "./Models/" + filename + "/"
        file = path + filename

        # values used to normalize training data
        max_vals = np.array([])
        min_vals = np.array([])

        with open(file + '.csv', "r") as f:
            f_data = list(csv.reader(f))
            max_vals = np.array([float(f.strip()) for f in f_data[0]])
            min_vals = np.array([float(f.strip()) for f in f_data[1]])

        return max_vals, min_vals