我创建了一个卷积神经网络的模型，我实现了训练，现在我必须创建一个在测试模式下运行模型的函数，但我不知道如何做到这一点。
我有两个数据集，一个用于训练，另一个用于测试，因此我应该找到一种方法来测试测试数据集中的模型。
我可以像训练数据集一样加载测试数据集，但后来我不知道如何对已训练的模型进行测试。
这是模型功能
import tensorflow as tf
def cnn_model_fn(X, MODE, log=False):
# INPUT LAYER
with tf.name_scope('input_layer') as scope:
input_layer = tf.reshape(X, [-1, 1000, 48, 1])
# CONVOLUTIONAL LAYER #1
with tf.name_scope('Conv1') as scope:
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=4,
kernel_size=[10, 10],
strides=(2, 2),
padding="valid",
)
if log==True:
print('[LOG:conv1]: ' + str(conv1.shape))
# apply the relu function
conv1_relu = tf.nn.relu(conv1)
if log==True:
print('[LOG:conv1_relu]: ' + str(conv1_relu.shape))
# POOLING LAYER #1
with tf.name_scope('Pool1'):
pool1 = tf.layers.max_pooling2d(
inputs=conv1_relu,
pool_size=[2, 2],
strides=2
)
if log==True:
print('[LOG:pool1]: ' + str(pool1.shape))
# CONVOLUTIONAL LAYER #2
with tf.name_scope('Conv2'):
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
)
if log==True:
print('[LOG:conv2]: ' + str(conv2.shape))
# apply the relu function
conv2_relu = tf.nn.relu(conv2)
if log==True:
print('[LOG:conv2_relu]: ' + str(conv2_relu.shape))
# POOLING LAYER #2
with tf.name_scope('Pool2'):
pool2 = tf.layers.max_pooling2d(
inputs=conv2_relu,
pool_size=[2, 2],
strides=2
)
if log==True:
print('[LOG:pool2]: ' + str(pool2.shape))
# create a variable with the pool2 size because I need it to calculate the pool2_flat size
x = tf.TensorShape.as_list(pool2.shape)
# REDENSIFY POOL2 TO REDUCE COMPUTATIONAL LOAD
with tf.name_scope('Reshape'):
pool2_flat = tf.reshape(pool2, [-1, x[1] * x[2] * x[3]])
if log==True:
print('[LOG:pool2_flat]: ' + str(pool2_flat.shape))
# DENSE LAYER
with tf.name_scope('Dense_layer'):
dense = tf.layers.dense(
inputs=pool2_flat,
units=1024,
)
if log==True:
print('[LOG:dense]: ' + str(dense.shape))
# apply the relu function
dense_relu = tf.nn.relu(dense)
if log==True:
print('[LOG:dense_relu]: ' + str(dense_relu.shape))
# add the dropout function
with tf.name_scope('Dropout'):
dropout = tf.layers.dropout(
inputs=dense_relu,
rate=0.4,
training=MODE == tf.estimator.ModeKeys.TRAIN
)
if log==True:
print('[LOG:dropout]: ' + str(dropout.shape))
# LOGIT LAYER
with tf.name_scope('Logit_layer'):
logits = tf.layers.dense(
inputs=dropout,
units=2
)
if log==True:
print('[LOG:logits]: ' + str(logits.shape))
return logits
这是主要计划
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# IMPORTS
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import os
import sys
from tqdm import tqdm
import load_dataset
import datetime
import time
get_images = load_dataset.get_images
next_batch = load_dataset.next_batch
import cnn_model_fn
cnn_model_fn = cnn_model_fn.cnn_model_fn
os.system('clear')
local_path = os.getcwd()
save_path = local_path + '/.Checkpoints/model.ckpt'
TensorBoard_path = local_path + "/.TensorBoard"
dataset_path = local_path + '/DATASET/'
#Training Parameters
learning_rate = 0.001
batch_size = 5
epochs = 2
MODE = 'TRAIN'
len_X, X, Y = get_images(
files_path=dataset_path,
img_size_h=1000,
img_size_w=48,
mode='TRAIN',
randomize=True
)
X_batch, Y_batch = next_batch(
total=len_X,
images=X,
labels=Y,
batch_size=batch_size,
index=0
)
logits = cnn_model_fn(X_batch, MODE)
prediction = tf.nn.softmax(logits)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=Y_batch))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss)
correct_predict = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y_batch, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))
init = tf.global_variables_initializer()
best_acc=0
with tf.Session() as sess:
sess.run(init)
saver = tf.train.Saver()
if MODE == 'TRAIN':
os.system('clear')
print("TRAINING MODE")
print('\n[epoch, iter]\t\tAccuracy\tProgress\tTime')
for step in range(1, epochs+1):
for i in range(0, int(len_X/batch_size)+1):
t0 = time.time()
X_batch, Y_batch = next_batch(
total=len_X,
images=X,
labels=Y,
batch_size=batch_size,
index=i
)
sess.run(train_op)
los, acc= sess.run([loss, accuracy])
t1 = time.time()
t = t1-t0
check = '[ ]'
if acc >= best_acc:
check = '[X]'
best_acc = acc
print('[e:' + str(step) + ', i:' + str(i) + ']\t\t' + '%.4f' % acc + '\t\t' + check + '\t\t' + '%.3f' % t + 's')
saver.save(sess,save_path)
else:
print('[e:' + str(step) + ', i:' + str(i) + ']\t\t' + '%.4f' % acc + '\t\t' + check + '\t\t' + '%.3f' % t + 's')
writer = tf.summary.FileWriter(TensorBoard_path, sess.graph)
elif MODE=='TEST':
os.system('clear')
print("TESTING MODE")
saver.restore(sess, save_path)
# here I need to test the model
sess.close()
非常感谢你的帮助和时间。
编辑：我解决了这个问题
saver.restore(sess, save_path)
print("Initialization Complete")
len_X_test, X_test, Y_test = get_images(
files_path=dataset_path,
img_size_h=img_size_h,
img_size_w=img_size_w,
mode='TEST',
randomize=True
)
train_feed = {x: X_test, y: Y_test}
print("Testing Accuracy:"+str(sess.run(accuracy, feed_dict=train_feed)))
您可以采用与模型培训相同的方式完成此操作。将测试分成批次并独立计算每个批次的损失和准确性。假设测试集长度可以被批量大小整除：
accuracies = []
losses = []
for i in range(0, len_X // batch_size + 1:
X_batch, Y_batch = next_batch(
total=len_X,
images=X,
labels=Y,
batch_size=batch_size,
index=i
)
los, acc= sess.run([loss, accuracy])
accuracies.append(acc)
losses.append(loss)
test_acc = np.mean(accuracies)
test_loss = np.mean(losses)
我解决了这个问题：
saver.restore(sess, save_path)
print("Initialization Complete")
len_X_test, X_test, Y_test = get_images(
files_path=dataset_path,
img_size_h=img_size_h,
img_size_w=img_size_w,
mode='TEST',
randomize=True
)
train_feed = {x: X_test, y: Y_test}
# test the model
print("Testing Accuracy:"+str(sess.run(accuracy, feed_dict=train_feed)))