import os
import tensorflow as tf
import numpy as np
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# Image Parameters
N_CLASSES = 2 # CHANGE HERE, total number of classes
IMG_HEIGHT = 128 # CHANGE HERE, the image height to be resized to
IMG_WIDTH = 128 # CHANGE HERE, the image width to be resized to
CHANNELS = 3 # The 3 color channels, change to 1 if grayscale
n_classes = N_CLASSES # MNIST total classes (0-9 digits)
dropout = 0.75
num_steps = 20000
display_step = 100
learning_rate = 0.01
BATCHSIZE=32
def _parse_function(record):
keys_to_features = {
'img_raw': tf.FixedLenFeature((), tf.string),
'label': tf.FixedLenFeature((), tf.int64)
}
parsed = tf.parse_single_example(record, keys_to_features)
image = tf.decode_raw(parsed['img_raw'], tf.uint8)
image = tf.reshape(image, [IMG_HEIGHT, IMG_WIDTH, 3])
image = tf.cast(image, tf.float32)
label = tf.cast(parsed['label'], tf.int32)
return image, label
# dataset pipeline
dataset = tf.data.TFRecordDataset("./01_cats_vs_dogs/train_dogs_cat.tfrecord")
dataset = dataset.map(_parse_function)
dataset = dataset.repeat()
dataset = dataset.batch(batch_size=BATCHSIZE)
dataset = dataset.prefetch(BATCHSIZE)
# Create an iterator over the dataset
iterator = dataset.make_one_shot_iterator()
X, Y = iterator.get_next()
# Neural Net Input (images, labels)
print(X.shape)
def conv_net(x, n_classes, dropout, reuse, is_training):
# Define a scope for reusing the variables
with tf.variable_scope('ConvNet', reuse=reuse):
# Convolution Layer with 32 filters and a kernel size of 5
# x = tf.reshape(x, shape=[-1, 64, 64, 3])
# Convolution Layer with 32 filters and a kernel size of 5
conv1 = tf.layers.conv2d(x, 64, 3, activation=tf.nn.relu)
conv1_1 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)
# Max Pooling (down-sampling) with strides of 2 and kernel size of 2
pool1 = tf.layers.max_pooling2d(conv1_1, 2, 2)
# Convolution Layer with 32 filters and a kernel size of 5
conv2_1 = tf.layers.conv2d(pool1, 128, 3, activation=tf.nn.relu)
conv2_2 = tf.layers.conv2d(conv2_1, 128, 3, activation=tf.nn.relu)
# Max Pooling (down-sampling) with strides of 2 and kernel size of 2
pool2 = tf.layers.max_pooling2d(conv2_2, 2, 2)
conv3_1 = tf.layers.conv2d(pool2, 512, 3, activation=tf.nn.relu)
conv3_2 = tf.layers.conv2d(conv3_1, 512, 3, activation=tf.nn.relu)
conv3_3 = tf.layers.conv2d(conv3_2, 512, 3, activation=tf.nn.relu)
conv3_4 = tf.layers.conv2d(conv3_3, 512, 3, activation=tf.nn.relu)
pool3 = tf.layers.max_pooling2d(conv3_4, 2, 2)
conv4_1 = tf.layers.conv2d(pool3, 512, 3, activation=tf.nn.relu)
conv4_2 = tf.layers.conv2d(conv4_1, 512, 3, activation=tf.nn.relu)
conv4_3 = tf.layers.conv2d(conv4_2, 512, 3, activation=tf.nn.relu)
conv4_4 = tf.layers.conv2d(conv4_3, 512, 3, activation=tf.nn.relu)
pool4 = tf.layers.max_pooling2d(conv4_4, 2, 2)
# Flatten the data to a 1-D vector for the fully connected layer
fc1 = tf.contrib.layers.flatten(pool4)
# Fully connected layer (in contrib folder for now)
fc1 = tf.layers.dense(fc1, 4096)
# Apply Dropout (if is_training is False, dropout is not applied)
fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)
fc2 = tf.layers.dense(fc1, 2048)
fc2 = tf.layers.dropout(fc2, rate=dropout, training=is_training)
# Output layer, class prediction
out = tf.layers.dense(fc2, n_classes)
# Because 'softmax_cross_entropy_with_logits' already apply softmax,
# we only apply softmax to testing network
out = tf.nn.softmax(out) if not is_training else out
# out = tf.nn.softmax(out)
return out
logits_train = conv_net(X, N_CLASSES, dropout, reuse=False, is_training=True)
logits_test = conv_net(X, N_CLASSES, dropout, reuse=True, is_training=False)
loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_train, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)
correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.cast(Y, tf.int64))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
# sess.run(iterator.initializer)
sess.run(init)
# Training cycle
for step in range(1, num_steps + 1):
sess.run(train_op)
if step % display_step == 0 or step == 1:
# Run optimization and calculate batch loss and accuracy
loss, acc = sess.run([loss_op, accuracy])
print("Step " + str(step) + ", Minibatch Loss= " + "{:.4f}".format(loss) + ", Training Accuracy= " +
"{:.3f}".format(acc))
print("Optimization Finished!")
saver.save(sess, './model1/my_tf_model.ckpt')
我将img数据转换为tfrecord，数据集包含两个cals，{'dog'，'cat'}，并且已验证tfrecord没问题
结果如下，当我训练模型时，我发现准确性似乎并没有改变，我不知道出了什么问题：
Step 100, Minibatch Loss= 328.0390, Training Accuracy= 0.375
Step 200, Minibatch Loss= 20.1806, Training Accuracy= 0.469
Step 300, Minibatch Loss= 8.0567, Training Accuracy= 0.594
Step 400, Minibatch Loss= 7.8446, Training Accuracy= 0.469
Step 500, Minibatch Loss= 8.1242, Training Accuracy= 0.562
Step 600, Minibatch Loss= 11.3462, Training Accuracy= 0.500
Step 700, Minibatch Loss= 6.3456, Training Accuracy= 0.656
Step 800, Minibatch Loss= 8.6048, Training Accuracy= 0.406
Step 900, Minibatch Loss= 5.7220, Training Accuracy= 0.500
Step 1000, Minibatch Loss= 6.6008, Training Accuracy= 0.594
Step 1100, Minibatch Loss= 10.1282, Training Accuracy= 0.469
Step 1200, Minibatch Loss= 9.9202, Training Accuracy= 0.375
Step 1300, Minibatch Loss= 7.2488, Training Accuracy= 0.562
Step 1400, Minibatch Loss= 5.7681, Training Accuracy= 0.406
Step 1500, Minibatch Loss= 6.8479, Training Accuracy= 0.719
Step 1600, Minibatch Loss= 4.2005, Training Accuracy= 0.562
Step 1700, Minibatch Loss= 6.7389, Training Accuracy= 0.531
Step 1800, Minibatch Loss= 5.1379, Training Accuracy= 0.250
Step 1900, Minibatch Loss= 5.5253, Training Accuracy= 0.562
Step 2000, Minibatch Loss= 10.0953, Training Accuracy= 0.562
Step 2100, Minibatch Loss= 4.0466, Training Accuracy= 0.531
Step 2200, Minibatch Loss= 7.2034, Training Accuracy= 0.562
答案 0 :(得分：1)
TLDR;
Conv2D层和Dense层单位的过滤器dropout参数，例如：0.2 lr=1e-4 dropout=0.2和一个更简单的模型的输出（设置在此答案的结尾）：
Step 1, Minibatch Loss= 20.6069, Training Accuracy= 0.625
Step 100, Minibatch Loss= 0.6134, Training Accuracy= 0.656
Step 200, Minibatch Loss= 0.6814, Training Accuracy= 0.625
Step 300, Minibatch Loss= 0.6467, Training Accuracy= 0.688
Step 400, Minibatch Loss= 0.6255, Training Accuracy= 0.625
Step 500, Minibatch Loss= 0.7261, Training Accuracy= 0.500
Step 600, Minibatch Loss= 0.6132, Training Accuracy= 0.656
Step 700, Minibatch Loss= 0.5459, Training Accuracy= 0.719
Step 800, Minibatch Loss= 0.6878, Training Accuracy= 0.688
Step 900, Minibatch Loss= 0.6291, Training Accuracy= 0.625
Step 1000, Minibatch Loss= 0.5999, Training Accuracy= 0.750
Step 1100, Minibatch Loss= 0.5825, Training Accuracy= 0.656
Step 1200, Minibatch Loss= 0.4984, Training Accuracy= 0.844
Step 1300, Minibatch Loss= 0.6453, Training Accuracy= 0.656
Step 1400, Minibatch Loss= 0.7097, Training Accuracy= 0.562
Step 1500, Minibatch Loss= 0.4389, Training Accuracy= 0.750
Step 1600, Minibatch Loss= 0.5230, Training Accuracy= 0.719
Step 1700, Minibatch Loss= 0.6794, Training Accuracy= 0.625
Step 1800, Minibatch Loss= 0.4587, Training Accuracy= 0.781
Step 1900, Minibatch Loss= 0.4308, Training Accuracy= 0.875
Step 2000, Minibatch Loss= 0.4679, Training Accuracy= 0.812
Step 2100, Minibatch Loss= 0.3197, Training Accuracy= 0.875
Step 2200, Minibatch Loss= 0.4301, Training Accuracy= 0.844
Step 2300, Minibatch Loss= 0.2784, Training Accuracy= 0.875
Step 2400, Minibatch Loss= 0.4588, Training Accuracy= 0.781
Step 2500, Minibatch Loss= 0.6086, Training Accuracy= 0.688
Step 2600, Minibatch Loss= 0.5364, Training Accuracy= 0.750
Step 2700, Minibatch Loss= 0.2958, Training Accuracy= 0.906
在步骤4900中，acc达到96.9％
Step 4900, Minibatch Loss= 0.1735, Training Accuracy= 0.969
为什么？
此外，最好将数据集分为训练和测试。
参考
https://www.quora.com/Why-does-my-convolutional-neural-network-always-produce-the-same-outputs
模型配置：
conv1 = tf.layers.conv2d(x, 16, 3, activation=tf.nn.relu)
conv1_1 = tf.layers.conv2d(conv1, 16, 3, activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(conv1_1, 2, 2)
conv2_1 = tf.layers.conv2d(pool1, 32, 3, activation=tf.nn.relu)
conv2_2 = tf.layers.conv2d(conv2_1, 32, 3, activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(conv2_2, 2, 2)
conv3_1 = tf.layers.conv2d(pool2, 64, 3, activation=tf.nn.relu)
conv3_2 = tf.layers.conv2d(conv3_1, 64, 3, activation=tf.nn.relu)
conv3_3 = tf.layers.conv2d(conv3_2, 128, 3, activation=tf.nn.relu)
conv3_4 = tf.layers.conv2d(conv3_3, 128, 3, activation=tf.nn.relu)
pool3 = tf.layers.max_pooling2d(conv3_4, 2, 2)
conv4_1 = tf.layers.conv2d(pool3, 512, 3, activation=tf.nn.relu)
conv4_2 = tf.layers.conv2d(conv4_1, 512, 3, activation=tf.nn.relu)
pool4 = tf.layers.max_pooling2d(conv4_2, 2, 2)
fc1 = tf.contrib.layers.flatten(pool4)
fc1 = tf.layers.dense(fc1, 512)
fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)
fc2 = tf.layers.dense(fc1, 128)
fc2 = tf.layers.dropout(fc2, rate=dropout, training=is_training)
out = tf.layers.dense(fc2, n_classes)
out = tf.nn.softmax(out) if not is_training else out