python深度学习之销量预测

# -*- coding: utf-8 -*-


import time
import warnings
import numpy as np
import matplotlib.pyplot as plt
from numpy import newaxis
from __future__ import print_function
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers.core import Dense, Activation, Dropout

def load_data(filename, seq_len, normalise_window):
  
f = open(filename, 'rb').read()
data = f.split('\n')
sequence_length = seq_len + 1
result = []
    
for index in range(len(data) - sequence_length):
      
result.append(data[index: index + sequence_length])  #得到长度为seq_len+1的向量，最后一个作为label

if normalise_window:
result = normalise_windows(result)
result = np.array(result)
            row = round(0.9 * result.shape[0])
train = result[:int(row), :]
np.random.shuffle(train)
x_train = train[:, :-1]
y_train = train[:, -1]
x_test = result[int(row):, :-1]
y_test = result[int(row):, -1]
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
return [x_train, y_train, x_test, y_test]


def normalise_windows(window_data):
  
normalised_data = []
    
for window in window_data:   #window shape (sequence_length L ,)  即(51L,)
normalised_window = [((float(p) / float(window[0])) - 1) for p in window]
normalised_data.append(normalised_window)
return normalised_data


def build_model(layers):  #layers [1,50,100,1]
model = Sequential()
model.add(LSTM(input_dim=layers[0],output_dim=layers[1],return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(layers[2],return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(output_dim=layers[3]))
model.add(Activation("linear"))
start = time.time()
model.compile(loss="mse", optimizer="rmsprop")
print("Compilation Time : ", time.time() - start)
return model


# 直接全部预测
def predict_point_by_point(model, data):
predicted = model.predict(data)
print('predicted shape:',np.array(predicted).shape)  #(412L,1L)
predicted = np.reshape(predicted, (predicted.size,))
return predicted


# 滚动预测
def predict_sequence_full(model, data, window_size):  #data X_test
curr_frame = data[0]  #(50L,1L)
predicted = []
for i in xrange(len(data)):
#x = np.array([[[1],[2],[3]], [[4],[5],[6]]])  x.shape (2, 3, 1) x[0,0] = array([1])  x[:,np.newaxis,:,:].shape  (2, 1, 3, 1)
predicted.append(model.predict(curr_frame[newaxis,:,:])[0,0])  #np.array(curr_frame[newaxis,:,:]).shape (1L,50L,1L)
curr_frame = curr_frame[1:]
curr_frame = np.insert(curr_frame, [window_size-1], predicted[-1], axis=0)   #numpy.insert(arr, obj, values, axis=None)
return predicted


def predict_sequences_multiple(model, data, window_size, prediction_len):  #window_size = seq_len
prediction_seqs = []
for i in xrange(len(data)/prediction_len):
curr_frame = data[i*prediction_len]
predicted = []
for j in xrange(prediction_len):
predicted.append(model.predict(curr_frame[newaxis,:,:])[0,0])
curr_frame = curr_frame[1:]
curr_frame = np.insert(curr_frame, [window_size-1], predicted[-1], axis=0)
prediction_seqs.append(predicted)
return prediction_seqs


def plot_results(predicted_data, true_data, filename):
fig = plt.figure(facecolor='white')
ax = fig.add_subplot(111)
ax.plot(true_data, label='True Data')
plt.plot(predicted_data, label='Prediction')
plt.legend()
plt.show()
plt.savefig(filename+'.png')


def plot_results_multiple(predicted_data, true_data, prediction_len):
fig = plt.figure(facecolor='white')
ax = fig.add_subplot(111)
ax.plot(true_data, label='True Data')
#Pad the list of predictions to shift it in the graph to it's correct start
for i, data in enumerate(predicted_data):
padding = [None for p in xrange(i * prediction_len)]
plt.plot(padding + data, label='Prediction')
plt.legend()
plt.show()
plt.savefig('plot_results_multiple.png')

if __name__=='__main__':
                                                              
global_start_time = time.time()
epochs  = 1
seq_len = 50
print('> Loading data... ')
X_train, y_train, X_test, y_test = load_data('dataset/sp500.csv', seq_len, True)
print('> Data Loaded. Compiling...')
model = build_model([1, 50, 100, 1])
model.fit(X_train,y_train,batch_size=512,nb_epoch=epochs,validation_split=0.05)
# multiple_predictions = predict_sequences_multiple(model, X_test, seq_len, prediction_len=50)
#full_predictions = predict_sequence_full(model, X_test, seq_len)
#print('full_predictions shape:',np.array(full_predictions).shape)    #(412L,)
point_by_point_predictions = predict_point_by_point(model, X_test)
#plot_results(full_predictions,y_test,'full_predictions')
plot_results(point_by_point_predictions,y_test,'point_by_point_predictions')

#测试在tensorflow 版本0.10.0中 需要改tensorflow_backend.py 中的两处代码后可用 版本兼用问题。

转自   http://www.cnblogs.com/arkenstone/p/5794063.html