Python Tensorflow CNN-密集层作为Softmax层输入
我正试图复制《纽约时报》中描述的CNN 我被困在最后一层。我是这样模仿cnn的Python Tensorflow CNN-密集层作为Softmax层输入,python,tensorflow,convolution,softmax,Python,Tensorflow,Convolution,Softmax,我正试图复制《纽约时报》中描述的CNN 我被困在最后一层。我是这样模仿cnn的 # Model function for CNN def cnn_model_fn(features, labels, mode): # Input Layer # Reshape X to 4-D tensor: [batch_size, width, height, channels] # Taxes images are 150x150 pixels, and have one color ch
# Model function for CNN
def cnn_model_fn(features, labels, mode):
# Input Layer
# Reshape X to 4-D tensor: [batch_size, width, height, channels]
# Taxes images are 150x150 pixels, and have one color channel
input_layer = tf.reshape(features, [-1, 150, 150, 1])
# Convolutional Layer #1
# Input Tensor Shape: [batch_size, 150, 150, 1]
# Output Tensor Shape: [batch_size, 144, 144, 20]
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=20,
kernel_size=[7, 7],
padding="valid",
activation=tf.nn.relu)
# Pooling Layer #1
# Input Tensor Shape: [batch_size, 144, 144, 20]
# Output Tensor Shape: [batch_size, 36, 36, 20]
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[4, 4], strides=4)
# Convolutional Layer #2
# Input Tensor Shape: [batch_size, 36, 36, 20]
# Output Tensor Shape: [batch_size, 32, 32, 50]
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=50,
kernel_size=[5, 5],
padding="valid",
activation=tf.nn.relu)
# Pooling Layer #2
# Input Tensor Shape: [batch_size, 32, 32, 50]
# Output Tensor Shape: [batch_size, 8, 8, 50]
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[4, 4], strides=4)
# Flatten tensor into a batch of vectors
# Input Tensor Shape: [batch_size, 8, 8, 50]
# Output Tensor Shape: [batch_size, 8 * 8 * 50]
pool2_flat = tf.reshape(pool2, [-1, 8 * 8 * 50])
# Dense Layer #1
# Densely connected layer with 1000 neurons
# Input Tensor Shape: [batch_size, 8 * 8 * 50]
# Output Tensor Shape: [batch_size, 1000]
dense1 = tf.layers.dense(inputs=pool2_flat, units=1000, activation=tf.nn.relu)
# Dense Layer #2
# Densely connected layer with 1000 neurons
# Input Tensor Shape: [batch_size, 1000]
# Output Tensor Shape: [batch_size, 1000]
dense2 = tf.layers.dense(inputs=dense1, units=1000, activation=tf.nn.relu)
# Add dropout operation; 0.5 probability that element will be kept
dropout = tf.layers.dropout(
inputs=dense2, rate=0.5, training=mode == learn.ModeKeys.TRAIN)
# Logits layer
# Input Tensor Shape: [batch_size, 1000]
# Output Tensor Shape: [batch_size, 4]
logits = tf.layers.dense(inputs=dropout, units=nClass)
loss = None
train_op = None
# Calculate Loss (for both TRAIN and EVAL modes)
if mode != learn.ModeKeys.INFER:
onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=nClass)
loss = tf.losses.softmax_cross_entropy(
onehot_labels=onehot_labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == learn.ModeKeys.TRAIN:
train_op = tf.contrib.layers.optimize_loss(
loss=loss,
global_step=tf.contrib.framework.get_global_step(),
learning_rate=0.001,
optimizer="SGD")
# Generate Predictions
predictions = {
"classes": tf.argmax(
input=logits, axis=1)
}
# Return a ModelFnOps object
return model_fn_lib.ModelFnOps(
mode=mode, predictions=predictions, loss=loss, train_op=train_op)
但最终的准确度非常差(0.25)。所以我意识到,实际上这篇论文说最后一层是softmax层。所以我试着把我的登录层改为
logits = tf.layers.softmax(dropout)
但当我运行它时,它会说
ValueError: Shapes (?, 1000) and (?, 4) are incompatible
那么,我在这里遗漏了什么呢?原来的答案是正确的。当使用
tf.loss.softmax\u cross\u熵计算损耗时,应用softmax激活。如果要单独计算,应在logits计算后添加,但不要像以前那样替换
logits = tf.layers.dense(inputs=dropout, units=nClass)
softmax = tf.layers.softmax(logits)
或者你可以把两者结合在一起,但我不推荐。最好用损耗计算softmax
logits = tf.layers.dense(inputs=dropout, units=nClass, activation=tf.layers.softmax)
您的分类器没有比random做得更好,所以我想说问题出在其他地方,可能在数据加载和预处理方面 原来的是正确的。当使用tf.loss.softmax\u cross\u熵计算损耗时,应用softmax激活。如果要单独计算,应在logits计算后添加,但不要像以前那样替换
logits = tf.layers.dense(inputs=dropout, units=nClass)
softmax = tf.layers.softmax(logits)
或者你可以把两者结合在一起,但我不推荐。最好用损耗计算softmax
logits = tf.layers.dense(inputs=dropout, units=nClass, activation=tf.layers.softmax)
您的分类器没有比random做得更好,所以我想说问题出在其他地方,可能在数据加载和预处理方面 你完全正确:在规范化输入之后,我开始得到正确的结果。非常感谢。你完全正确:在规范化输入后,我开始得到正确的结果。谢谢。