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Created?on?16/02/2017
@author:?smas255
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import?tensorflow?as?tf
import?numpy?as?np
from?sklearn?import?preprocessing
from?sklearn.model_selection?import?train_test_split
from?sklearn?import?metrics
import?math
from?matplotlib?import?pyplot
#?Read?data?from?csv?file
def?get_data（filename）:????
????#?Read?file
????priceDataset=np.loadtxt（open（filename?“rb“）?delimiter=““?skiprows=1）
????#?Scale?data???
????scaler?=?preprocessing.MinMaxScaler（feature_range=（0?1））????
????X_dataset=scaler.fit_transform（priceDataset[...0]）
????Y_dataset=scaler.fit_transform（priceDataset[...1]）
????#?Split?data?to?training?and?test?
????X_train?X_test?y_train?y_test?=?train_test_split（
????????X_dataset?Y_dataset?test_size=0.3?random_state=1234）
????return?scalerX_train?X_test?y_train?y_test
#?Create?model
def?multilayer_perceptron（x?weights?biases）:
????#?Hidden?layer?with?Tanh?activation
????layer_1?=?tf.add（tf.matmul（x?weights[‘h1‘]）?biases[‘b1‘]）
????layer_1?=?tf.nn.tanh（layer_1）

????#?Hidden?layer?with?Tanh?activation
????layer_2?=?tf.add（tf.matmul（layer_1?weights[‘h2‘]）?biases[‘b2‘]）
????layer_2?=?tf.nn.tanh（layer_2）???

????#?Output?layer?with?linear?activation
????out_layer?=?tf.matmul（layer_2?weights[‘out‘]）?+?biases[‘out‘]
????return?out_layer
if?__name__?==?‘__main__‘:??
????#?Create?training?and?test?
????scaler?x_train?x_test?y_train?y_test?=get_data（‘./data/aapl.csv‘）???
????#?Reshape?data?for?a?network?with?single?input?and?output
????x_train=np.reshape（x_train?（-1?1））
????y_train=np.reshape（y_train?（-1?1））
????x_test=np.reshape（x_test?（-1?1））
????y_test=np.reshape（y_test?（-1?1））
????#?Get?size?of?training?
????total_len?=?x_train.shape[0]
????#?Parameters
????learning_rate?=?0.01
????training_epochs?=?700
????batch_size?=?5
????display_step?=?1
????
????#?Network?Parameters
????n_hidden_1?=?15?#?1st?layer?number?of?features
????n_hidden_2?=?7?#?2nd?layer?number?of?features????
????n_input?=?x_train.shape[1]
????n_output?=?1
????
????#?tf?Graph?input
????x?=?tf.placeholder（“float“?[None?1]）
????y?=?tf.placeholder（“float“?[None1]）
????
????#?Create?weights?and?bias?vector?with?constant?data?to?be?similar?to?Azure?machine?learning?service?
????weights_1?=?np.empty（[n_input?n_hidden_1]dtype=np.float32）
????weights_2?=?np.empty（[n_hidden_1?n_hidden_2]dtype=np.float32）
????weights_3?=?np.empty（[n_hidden_2?n_output]dtype=np.float32）
????weights_1.fill（0.1）
????weights_2.fill（0.1）
????weights_3.fill（0.1）
????bias_1=np.empty（n_hidden_1dtype=np.float32）
????bias_2=np.empty（n_hid