rng = np.random.RandomState(888)
# np.random.seed(423453)
base_dir = os.getcwd()
# train_csv_path = os.path.join(base_dir,'trainingData.csv')
test_csv_path = os.path.join(base_dir, 'validationData.csv')
valid_csv_path = os.path.join(base_dir, 'AllValuationData.csv')
train_csv_path = os.path.join(base_dir, 'arrAllTrainingData.csv')
log_dir = 'DEEPLEARNING_MODEL_log.txt'
if __name__ == '__main__':
# Load data
(train_x,
train_y), (valid_x,
valid_y), (test_x, test_y) = data_helper.load_data_all(
train_csv_path, valid_csv_path, test_csv_path)
# train_x, train_y, valid_x, valid_y, test_x, test_y=data_helper.load(train_csv_path, test_csv_path)
# patience=[i for i in range(1,50,2)]
# patience=[21,]
# B=[i for i in np.linspace(3.0,3.1,2)]
# for b in B:
# for p in patience:
# dropout=[i for i in np.linspace(0.4,0.7,4)]
dropout = [
0.7,
]
for dp in dropout:
#!/usr/bin/env python
import os
import time
from model_patient import *
from data_helper import load_data, load_data_all
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
batch_size = 128
test_size = 256
allX, allY = load_data_all()
word_embedding = np.load('../data/model_50.npy')
# print teY
# raw_input()
py_x, cost = model()
# cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y))
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
predict_op = py_x
# print trY.shape
# Launch the graph in a session
with tf.Session() as sess:
# you need to initialize all variables
tf.initialize_all_variables().run()