add files

serve.py

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+import tensorflow as tf
+import numpy as np
+import tushare as ts
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+
+timesteps = seq_length = 7
+data_dim = 5
+output_dim = 1
+
+stock_data = ts.get_k_data('600000', start='2015-01-01', end='2017-12-01')
+xy = stock_data[['open', 'close', 'high', 'low', 'volume']]
+
+# xy_new = pd.DataFrame()
+# scaler = MinMaxScaler()
+
+# scaler.fit(xy)
+# t = scaler.transform(xy)
+
+# for col in xy.columns:
+#	xy_new.ix[:, col] = t[col]
+
+x = xy
+y = xy[['close']]
+dataX = []
+dataY = []
+for i in range(0, len(y) - seq_length):
+	_x = x[i:i + seq_length]
+	_y = y.loc[i + seq_length]
+	#print(_x, "->", _y)
+	dataX.append(_x)
+	dataY.append(_y)
+
+x_real = np.vstack(dataX).reshape(-1, seq_length, data_dim)
+y_real = np.vstack(dataY).reshape(-1, output_dim)
+print(x_real.shape)
+print(y_real.shape)
+dataX = x_real
+dataY = y_real
+
+train_size = int(len(dataY) * 0.7)
+test_size = len(dataY) - train_size
+trainX, testX = np.array(dataX[0:train_size]), np.array(dataX[train_size:len(dataX)])
+trainY, testY = np.array(dataY[0:train_size]), np.array(dataY[train_size:len(dataY)])
+
+X = tf.placeholder(tf.float32, [None, seq_length, data_dim])
+Y = tf.placeholder(tf.float32, [None, 1])
+
+
+def add_layer(inputs, in_size, out_size, activation_function=None):
+	inputs = tf.reshape(inputs, [-1, in_size])
+	Weights = tf.Variable(tf.random_normal([in_size, out_size]))
+	biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
+	Wx_plus_b = tf.matmul(inputs, Weights) + biases
+	if activation_function is None:
+		outputs = Wx_plus_b
+	else:
+		outputs = activation_function(Wx_plus_b)
+	return outputs
+
+
+outsize_first = 5
+l1 = add_layer(X, data_dim, outsize_first, activation_function=tf.nn.relu)
+l1_output = tf.reshape(l1, [-1, seq_length, outsize_first])
+
+cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=output_dim, state_is_tuple=True)
+outputs, _states = tf.nn.dynamic_rnn(cell, l1_output, dtype=tf.float32)
+Y_pred = outputs[:, -1]
+
+loss = tf.reduce_sum(tf.square(Y_pred - Y))
+
+optimizer = tf.train.GradientDescentOptimizer(0.01)
+train = optimizer.minimize(loss)
+
+sess = tf.Session()
+sess.run(tf.global_variables_initializer())
+for i in range(100):
+	_, l = sess.run(
+		[train, loss],
+		feed_dict={X: trainX, Y: trainY}
+	)
+	#print(i, l)
+
+testPredict = sess.run(Y_pred, feed_dict={X: testX})
+
+print(testY)
+print(testPredict)