SIZE = 128
learningRate = 0.001
epoch = 10000
disp = 10
batchSize = 200
classNum = 2
logs_path = "C:/tf_log/dog_cat/"
keepprob = 0.90
with tf.name_scope("model"):
imgBatch, labelBatch = inputBatch("./data/train.tfrecords", batchSize,
SIZE)
labelBatch = tf.one_hot(labelBatch, depth=2, on_value=1, off_value=0)
alexnet = AlexNet(classNum, keepprob)
pred = alexnet.createNetwork2(imgBatch)
with tf.name_scope("loss"):
vars = tf.trainable_variables()
lossL2 = tf.add_n([tf.nn.l2_loss(v) for v in vars]) * 0.0001
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=pred, labels=labelBatch)) + lossL2
optimizer = tf.train.AdamOptimizer(
learning_rate=learningRate).minimize(cost)
tf.summary.scalar("loss", cost)
with tf.name_scope("eval"):
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(labelBatch, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
tf.summary.scalar("acc", accuracy)
SIZE = 128
batchSize = 100
classNum = 2
keepprob = 0.95
pictureNum = 12500
csvfile = open('./result/csv_test3.csv', 'w', newline='')
writer = csv.writer(csvfile)
writer.writerow(['id', 'label'])
with tf.name_scope("model"):
img = inputNoShuffle("./data/test.tfrecords", SIZE)
alexnet = AlexNet(classNum, keepprob)
pred = alexnet.createNetwork2(img, testFlag=True)
ans = tf.argmax(pred, axis=1)
with tf.Session() as sess:
saver = tf.train.Saver()
saver.restore(sess, "./model_save/model5.ckpt")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(pictureNum):
ansArr, predArr = sess.run([ans, pred])
print(ansArr, predArr)
writer.writerow([str(i + 1), float(predArr[0, 1])])
