我正在尝试用TensorFlow创建一个CNN，它将来自google tutorials on CNNs的图像分类。我创建了一个加载图像数据集的function和一个提取训练批次的#Training Parameters
learning_rate = 0.001
batch_size = 128
epochs = 10
MODE = 'TRAIN'
# Function that loads the entire dataset of images (X) with their respective labels (Y). X and Y are two np.array
len_X, X, Y = get_images(
files_path=dataset_path,
img_size_h=1000,
img_size_w=48,
mode='TRAIN',
randomize=True
)
# Function that load the batch from X and Y
X_batch, Y_X_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_X_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_X_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':
print("TRAINING MODE")
for step in range(1,epochs+1):
for i in range(0, int(len_X/batch_size)+1):
if i > 0:
X_batch, Y_X_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])
if acc >= best_acc:
best_acc = acc
writer = tf.summary.FileWriter(TensorBoard_path, sess.graph)
elif MODE=='TEST':
# TEST MODE #
sess.close()
。但即使我转到下一批，网络总是在同一批次上进行训练。数据集有10000个图像。我认为该模型不会进行下一批，因为我在不到10次迭代中达到100％的准确度。这里的代码：
def cnn_model_fn(X, MODE):
# INPUT LAYER
input_layer = tf.reshape(X, [-1, 1000, 48, 1])
# CONVOLUTIONAL LAYER #1
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=4,
kernel_size=[10, 10],
strides=(2, 2),
padding="valid",
)
conv1_relu = tf.nn.relu(conv1)
# POOLING LAYER #1
pool1 = tf.layers.max_pooling2d(
inputs=conv1_relu,
pool_size=[2, 2],
strides=2
)
# CONVOLUTIONAL LAYER #2
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
)
conv2_relu = tf.nn.relu(conv2)
# POOLING LAYER #2
pool2 = tf.layers.max_pooling2d(
inputs=conv2_relu,
pool_size=[2, 2],
strides=2
)
x = tf.TensorShape.as_list(pool2.shape)
pool2_flat = tf.reshape(pool2, [-1, x[1] * x[2] * x[3]])
# DENSE LAYER
dense = tf.layers.dense(
inputs=pool2_flat,
units=1024,
)
dense_relu = tf.nn.relu(dense)
# AGGIUNGO L'OPERAZIONE DI DROPOUT
dropout = tf.layers.dropout(
inputs=dense_relu,
rate=0.4,
training=MODE == tf.estimator.ModeKeys.TRAIN
)
# LOGIT LAYER
logits = tf.layers.dense(
inputs=dropout,
units=2
)
return logits
这是神经网络模型的结构：
Y_batch_placeholder = tf.placeholder(tf.float32 ,[None, 2] )
X_batch_placeholder = tf.placeholder(tf.float32 ,[None, 1000, 48])
logits = cnn_model_fn(X_batch_placeholder, MODE)
prediction = tf.nn.softmax(logits)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=Y_batch_placeholder))
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_placeholder, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))
非常感谢您的参与。
您没有使用tensorflow占位符。确保在您的损失函数中定义并使用X_batch_placeholder和Y_batch_placeholder，如下所示：
feed_dict然后使用feed_dict运行您的会话。一些张量流变量是占位符，您需要使用_, los, acc = sess.run([train_op, loss, accuracy],
feed_dict={X_batch_placeholder: X_batch, Y_batch_placeholder: Y_batch})
在运行会话时为它们提供相关信息：
Tensorflow: When should I use or not use `feed_dict`?
看到这里：https://hanxiao.github.io/2017/07/07/Get-10x-Speedup-in-Tensorflow-Multi-Task-Learning-using-Python-Multiprocessing/和这里：qazxswpoi