我有一个LSTM，我跑了两遍我的剧本里（获得两次预测为好）。所以，当我从第二个迭代的预测是从我从第一次迭代接收的预测不同。但是，当我通过运行在第一个完成当次手动运行模型两次。结果相同。
问：我想在第一和第二次迭代都一样相同的预测。如何做到这一点？下面显示的是我的代码。
import tensorflow as tf
import matplotlib as mplt
mplt.use('agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from math import sqrt
import csv
import atexit
from time import time, strftime, localtime
from datetime import timedelta
np.random.seed(1)
tf.set_random_seed(1)
class RNNConfig():
input_size = 1
noTimesToRun = 2
# fileNames = ['store2_1.csv', 'store85_1.csv', 'store259_1.csv', 'store519_1.csv', 'store725_1.csv',
# 'store749_1.csv',
# 'store934_1.csv', 'store1019_1.csv']
# column_min_max_all = [[[0, 11000], [1, 7]], [[0, 17000], [1, 7]], [[0, 23000], [1, 7]], [[0, 14000], [1, 7]],
# [[0, 14000], [1, 7]], [[0, 15000], [1, 7]], [[0, 17000], [1, 7]], [[0, 25000], [1, 7]]]
columns = ['Sales', 'DayOfWeek', 'SchoolHoliday', 'Promo', 'lagged_Open', 'lagged_promo', 'lagged_SchoolHoliday']
# fileNames = ['store85_1', 'store519_1', 'store725_1', 'store749_1','store165_1','store925_1','store1089_1','store335_1']
# column_min_max_all = [[[0, 17000], [1, 7]], [[0, 14000], [1, 7]], [[0, 14000], [1, 7]], [[0, 15000], [1, 7]],[[0, 9000], [1, 7]], [[0, 15000], [1, 7]], [[0, 21000], [1, 7]], [[0, 33000], [1, 7]]]
fileNames = ['store85_1']
column_min_max_all = [[[0, 17000], [1, 7]]]
features = len(columns)
num_steps = None
lstm_size = None
batch_size = None
init_learning_rate = None
learning_rate_decay = None
init_epoch = None
max_epoch = None
hidden1_nodes = None
hidden2_nodes = None
dropout_rate = None
hidden1_activation = None
hidden2_activation = None
lstm_activation = None
fileName = None
column_min_max = None
# plotname = None
writename = None
RMSE = None
MAE = None
MAPE = None
RMSPE = None
config = RNNConfig()
def secondsToStr(elapsed=None):
if elapsed is None:
return strftime("%Y-%m-%d %H:%M:%S", localtime())
else:
return str(timedelta(seconds=elapsed))
def log(s, elapsed=None):
line = "="*40
print(line)
print(secondsToStr(), '-', s)
if elapsed:
print("Elapsed time:", elapsed)
print(line)
print()
def endlog():
end = time()
elapsed = end-start
log("End Program", secondsToStr(elapsed))
def segmentation(data):
seq = [price for tup in data[config.columns].values for price in tup]
seq = np.array(seq)
# split into items of features
seq = [np.array(seq[i * config.features: (i + 1) * config.features])
for i in range(len(seq) // config.features)]
# split into groups of num_steps
X = np.array([seq[i: i + config.num_steps] for i in range(len(seq) - config.num_steps)])
y = np.array([seq[i + config.num_steps] for i in range(len(seq) - config.num_steps)])
# get only sales value
y = [[y[i][0]] for i in range(len(y))]
y = np.asarray(y)
print(y)
return X, y
def scale(data):
for i in range (len(config.column_min_max)):
data[config.columns[i]] = (data[config.columns[i]] - config.column_min_max[i][0]) / ((config.column_min_max[i][1]) - (config.column_min_max[i][0]))
return data
def rescle(test_pred):
prediction = [(pred * (config.column_min_max[0][1] - config.column_min_max[0][0])) + config.column_min_max[0][0] for pred in test_pred]
return prediction
def pre_process():
store_data = pd.read_csv(config.fileName)
store_data['lagged_Open'] = store_data['lagged_Open'].astype(int)
store_data['lagged_promo'] = store_data['lagged_promo'].astype(int)
store_data['lagged_SchoolHoliday'] = store_data['lagged_SchoolHoliday'].astype(int)
#
# store_data = store_data.drop(store_data[(store_data.Open != 0) & (store_data.Sales == 0)].index)
# ---for segmenting original data --------------------------------
# original_data = store_data.copy()
## train_size = int(len(store_data) * (1.0 - test_ratio))
# test_len = len(store_data[(store_data.Month == 7) & (store_data.Year == 2015)].index)
# train_size = int(len(store_data) - (test_len))
#
# train_data = store_data[:train_size]
# test_data = store_data[(train_size - config.num_steps):]
# original_test_data = test_data.copy()
#
# # -------------- processing train data---------------------------------------
# scaled_train_data = scale(train_data)
# train_X, train_y = segmentation(scaled_train_data)
#
# # -------------- processing test data---------------------------------------
# scaled_test_data = scale(test_data)
# test_X, test_y = segmentation(scaled_test_data)
#
# # ----segmenting original test data---------------------------------------------
# nonescaled_test_X, nonescaled_test_y = segmentation(original_test_data)
validation_len = len(store_data[(store_data.Month == 6) & (store_data.Year == 2015)].index)
test_len = len(store_data[(store_data.Month == 7) & (store_data.Year == 2015)].index)
train_size = int(len(store_data) - (validation_len + test_len))
train_data = store_data[:train_size]
validation_data = store_data[(train_size - config.num_steps): validation_len + train_size]
test_data = store_data[((validation_len + train_size) - config.num_steps):]
original_val_data = validation_data.copy()
original_test_data = test_data.copy()
# -------------- processing train data---------------------------------------
scaled_train_data = scale(train_data)
train_X, train_y = segmentation(scaled_train_data)
# -------------- processing validation data---------------------------------------
scaled_validation_data = scale(validation_data)
val_X, val_y = segmentation(scaled_validation_data)
# -------------- processing test data---------------------------------------
scaled_test_data = scale(test_data)
test_X, test_y = segmentation(scaled_test_data)
# ----segmenting original validation data-----------------------------------------------
nonescaled_val_X, nonescaled_val_y = segmentation(original_val_data)
# ----segmenting original test data-----------------------------------------------
nonescaled_test_X, nonescaled_test_y = segmentation(original_test_data)
return train_X, train_y, test_X, test_y, val_X, val_y, nonescaled_test_y, nonescaled_val_y
def generate_batches(train_X, train_y, batch_size):
num_batches = int(len(train_X)) // batch_size
if batch_size * num_batches < len(train_X):
num_batches += 1
batch_indices = range(num_batches)
for j in batch_indices:
batch_X = train_X[j * batch_size: (j + 1) * batch_size]
batch_y = train_y[j * batch_size: (j + 1) * batch_size]
assert set(map(len, batch_X)) == {config.num_steps}
yield batch_X, batch_y
def mean_absolute_percentage_error(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
itemindex = np.where(y_true == 0)
y_true = np.delete(y_true, itemindex)
y_pred = np.delete(y_pred, itemindex)
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
def RMSPE(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
itemindex = np.where(y_true == 0)
y_true = np.delete(y_true, itemindex)
y_pred = np.delete(y_pred, itemindex)
return np.sqrt(np.mean(np.square(((y_true - y_pred) / y_true)), axis=0))
# def plot(true_vals,pred_vals,name):
# fig = plt.figure()
# fig = plt.figure(dpi=100, figsize=(20, 7))
# days = range(len(true_vals))
# plt.plot(days, pred_vals, label='pred sales')
# plt.plot(days, true_vals, label='truth sales')
# plt.legend(loc='upper left', frameon=False)
# plt.xlabel("day")
# plt.ylabel("sales")
# plt.grid(ls='--')
# plt.savefig(name, format='png', bbox_inches='tight', transparent=False)
# plt.close()
def write_results(true_vals,pred_vals,name):
print("write method")
# with open(name, "w") as f:
# writer = csv.writer(f)
# writer.writerows(zip(true_vals, pred_vals))
def train_test():
train_X, train_y, test_X, test_y, val_X, val_y, nonescaled_test_y, nonescaled_val_y = pre_process()
# tf.set_random_seed(1)
inputs = tf.placeholder(tf.float32, [None, config.num_steps, config.features], name="inputs")
targets = tf.placeholder(tf.float32, [None, config.input_size], name="targets")
model_learning_rate = tf.placeholder(tf.float32, None, name="learning_rate")
model_dropout_rate = tf.placeholder_with_default(0.0, shape=())
global_step = tf.Variable(0, trainable=False)
model_learning_rate = tf.train.exponential_decay(learning_rate=model_learning_rate, global_step=global_step,
decay_rate=config.learning_rate_decay,
decay_steps=config.init_epoch, staircase=False)
cell = tf.contrib.rnn.LSTMCell(config.lstm_size, state_is_tuple=True, activation=config.lstm_activation,
use_peepholes=True)
val1, _ = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
val = tf.transpose(val1, [1, 0, 2])
last = tf.gather(val, int(val.get_shape()[0]) - 1, name="last_lstm_output")
# hidden layer
hidden1 = tf.layers.dense(last, units=config.hidden1_nodes, activation=config.hidden2_activation)
hidden2 = tf.layers.dense(hidden1, units=config.hidden2_nodes, activation=config.hidden1_activation)
dropout = tf.layers.dropout(hidden2, rate=model_dropout_rate, training=True,seed=1)
weight = tf.Variable(tf.truncated_normal([config.hidden2_nodes, config.input_size]))
bias = tf.Variable(tf.constant(0.1, shape=[config.input_size]))
prediction = tf.nn.relu(tf.matmul(dropout, weight) + bias)
loss = tf.losses.mean_squared_error(targets, prediction)
optimizer = tf.train.AdamOptimizer(model_learning_rate)
minimize = optimizer.minimize(loss, global_step=global_step)
# --------------------training------------------------------------------------------
tf.set_random_seed(1)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
iteration = 1
for epoch_step in range(config.max_epoch):
for batch_X, batch_y in generate_batches(train_X, train_y, config.batch_size):
train_data_feed = {
inputs: batch_X,
targets: batch_y,
model_learning_rate: config.init_learning_rate,
model_dropout_rate: config.dropout_rate
}
train_loss, _, value = sess.run([loss, minimize, val1], train_data_feed)
if iteration % 5 == 0:
print("Epoch: {}/{}".format(epoch_step, config.max_epoch),
"Iteration: {}".format(iteration),
"Train loss: {:.6f}".format(train_loss))
iteration += 1
saver = tf.train.Saver()
saver.save(sess, "checkpoints_sales/sales_pred.ckpt")
# --------------------testing------------------------------------------------------
saver.restore(sess, tf.train.latest_checkpoint('checkpoints_sales'))
test_data_feed = {
inputs: test_X,
}
test_pred = sess.run(prediction, test_data_feed)
# rmsse = sess.run(correct_prediction, test_data_feed)
pred_vals = rescle(test_pred)
pred_vals = np.array(pred_vals)
pred_vals = (np.round(pred_vals, 0)).astype(np.int32)
pred_vals = pred_vals.flatten()
pred_vals = pred_vals.tolist()
nonescaled_y = nonescaled_test_y.flatten()
nonescaled_y = nonescaled_y.tolist()
# plot(nonescaled_y, pred_vals, config.plotname)
# write_results(nonescaled_y, pred_vals, config.writename)
meanSquaredError = mean_squared_error(nonescaled_y, pred_vals)
rootMeanSquaredError = sqrt(meanSquaredError)
print("RMSE:", rootMeanSquaredError)
mae = mean_absolute_error(nonescaled_y, pred_vals)
print("MAE:", mae)
mape = mean_absolute_percentage_error(nonescaled_y, pred_vals)
print("MAPE:", mape)
rmse_val = RMSPE(nonescaled_y, pred_vals)
print("RMSPE:", rmse_val)
config.RMSE = rootMeanSquaredError
config.MAE = mae
config.MAPE = mape
config.RMSPE = rmse_val
# sess.close()
# tf.reset_default_graph()
if __name__ == '__main__':
start = time()
for i in range(len(config.fileNames)):
for j in range (config.noTimesToRun):
config.fileName = '{}{}{}'.format('/home/suleka/Documents/sales_prediction/', config.fileNames[i], '.csv')
# /home/suleka/Documents/sales_prediction/
# '/home/wso2/suleka/salesPred/
# config.plotname = '{}{}{}'.format('Sales_Prediction_testset_with_zero_bsl_plot_', config.fileNames[i],
# '.png')
config.writename = '{}{}{}{}{}'.format('prediction_data/Sales_Prediction_testset_with_zero_bsl_results_',j ,'_',config.fileNames[i],'.csv')
write_file = '{}{}{}{}{}'.format('test_results/test__data_',j,'_' ,config.fileNames[i], '.csv')
config.column_min_max = config.column_min_max_all[i]
hyperparameters = pd.read_csv('allStores_test.csv', header=None, float_precision='round_trip')
config.num_steps = hyperparameters.iloc[i:, 1].get_values()[0].astype(np.int32)
config.lstm_size = hyperparameters.iloc[i:, 2].get_values()[0].astype(np.int32)
config.hidden2_nodes = hyperparameters.iloc[i:, 3].get_values()[0].astype(np.int32)
config.hidden2_activation = hyperparameters.iloc[i:, 4].get_values()[0]
config.hidden1_activation = hyperparameters.iloc[i:, 5].get_values()[0]
config.hidden1_nodes = hyperparameters.iloc[i:, 6].get_values()[0].astype(np.int32)
config.lstm_activation = hyperparameters.iloc[i:, 7].get_values()[0]
config.init_epoch = hyperparameters.iloc[i:, 8].get_values()[0].astype(np.int32)
config.max_epoch = hyperparameters.iloc[i:, 9].get_values()[0].astype(np.int32)
config.learning_rate_decay = hyperparameters.iloc[i:, 10].get_values()[0].astype(np.float32)
config.dropout_rate = hyperparameters.iloc[i:, 11].get_values()[0].astype(np.float32)
config.batch_size = hyperparameters.iloc[i:, 12].get_values()[0].astype(np.int32)
config.init_learning_rate = hyperparameters.iloc[i:, 13].get_values()[0].astype(np.float32)
config.hidden1_activation = eval(config.hidden1_activation)
config.hidden2_activation = eval(config.hidden2_activation)
config.lstm_activation = eval(config.lstm_activation)
train_test()
tf.reset_default_graph()
atexit.register(endlog)
log("Start Program")
答案 0 :(得分：1)
请确保您始终在做这个顺序如下：
# Reset the default graph
tf.reset_default_graph()
# Set the random seed
tf.set_random_seed(seed)
# Build the graph
# ....
# After creating the cell make sure intialize it
cell.build(inputs_shape)
# Initialize all variables in the graph
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Train the model
# ....
# Evaluate the model
# ....
在你的代码中，我看到你第一次设置随机种子后正在重置图形。