我试图复制中描述的CNN https://pdfs.semanticscholar.org/3b57/85ca3c29c963ae396c2f94ba1a805c787cc8.pdf
我被困在最后一层。我已经像这样建立了cnn
# 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)
但最终的准确性非常差(0.25)。所以我意识到实际上论文指出最后一层是softmax层。所以我尝试将我的logits图层更改为
logits = tf.layers.softmax(dropout)
但是当我运行它时,它说
ValueError: Shapes (?, 1000) and (?, 4) are incompatible
那么,我在这里失踪了什么?
答案 0 :(得分:2)
原始的是正确的。在使用tf.losses.softmax_cross_entropy
计算损失时应用softmax激活。如果你想单独计算它,你应该在logits计算后添加它,但不要像你那样替换它。
logits = tf.layers.dense(inputs=dropout, units=nClass)
softmax = tf.layers.softmax(logits)
或者你可以将两者合二为一,但我不推荐它。最好用损耗计算softmax。
logits = tf.layers.dense(inputs=dropout, units=nClass, activation=tf.layers.softmax)
你的分类器并不比随机更好,所以我会说问题出现在其他地方,可能是在数据加载和预处理中。