我如何获得图像识别的概率

Question

I have an MNIST CNN. the network learning and training from the MNIST dataset and giving a vector of 10 probabilities for each digit (zero to nine), which sums up to 1 (using the softmax of course). I'm trying to change in a way that I will get ten probabilities for each digit, for example, the probability for a chosen image to b 1 is 0.23 so the probability that it doesn't 1 is 0.67, (also sums to 1 but for 10 digits). so what I need is 10 different softmax activations, but I cant understand how to do it. this is the original code which calculates 10 probabilities that add up to 1 and eventually gives a calculation of accuracy. there is a way to change the code to give a 10 softmax for each digit?

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf

tf.logging.set_verbosity(tf.logging.INFO)


def cnn_model_fn(features, labels, mode):

   input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])

   conv1 = tf.layers.conv2d(inputs=input_layer, filters=32,kernel_size[5,5], 
   padding="same", activation=tf.nn.relu)

   pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2],strides=2)

   conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[5, 5], 
   padding="same", activation=tf.nn.relu)

   pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2],strides=2)
   pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])

   dense = tf.layers.dense(inputs=pool2_flat, 
   units=1024,activation=tf.nn.relu)

   dropout = tf.layers.dropout(inputs=dense, rate=0.4, training=mode == 
   tf.estimator.ModeKeys.TRAIN)

   logits = tf.layers.dense(inputs=dropout, units=10)

   predictions = {
       "classes": tf.argmax(input=logits, axis=1),
       "probabilities": tf.nn.softmax(logits, name="softmax_tensor")}
   if mode == tf.estimator.ModeKeys.PREDICT:
     return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

   loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, 
   logits=logits)


   if mode == tf.estimator.ModeKeys.TRAIN:
     optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
     train_op = optimizer.minimize(
         loss=loss,
         global_step=tf.train.get_global_step())
     return tf.estimator.EstimatorSpec(mode=mode, loss=loss, 
     train_op=train_op)

   eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(
            labels=labels, predictions=predictions["classes"])}
   return tf.estimator.EstimatorSpec(
       mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


def main(unused_argv):
  # Load training and eval data
  mnist = tf.contrib.learn.datasets.load_dataset("mnist")
  train_data = mnist.train.images  # Returns np.array
  train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
  eval_data = mnist.test.images  # Returns np.array
  eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

  # Create the Estimator
  mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn, 
  model_dir="/tmp/mnist_convnet_model")

  # Set up logging for predictions
  # Log the values in the "Softmax" tensor with label "probabilities"
  tensors_to_log = {"probabilities": "softmax_tensor"}
  logging_hook = tf.train.LoggingTensorHook(
      tensors=tensors_to_log, every_n_iter=50)

  # Train the model
  train_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={"x": train_data},
      y=train_labels,
      batch_size=100,
      num_epochs=None,
      shuffle=True)
  mnist_classifier.train(
      input_fn=train_input_fn,
      steps=20000,
      hooks=[logging_hook])

  # Evaluate the model and print results
  eval_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={"x": eval_data},
      y=eval_labels,
      num_epochs=1,
      shuffle=False)
  eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
  print(eval_results)

if __name__ == "__main__":
  tf.app.run()

Answer 1

If I understand your question correctly is that you want independent predictions for each class.

The typical way to do that is to use sigmoid instead of softmax for activation and log_loss for the loss.

Now each class will be predicted independent of the others, so the probabilities will not sum up to 1.

You don't need a separate negative class in this setup. You can interpret 1-prediciton as the probability for the negative case (the image is not 1 for example).

Please note that this approach works best when you want to allow multiple labels for an image (the image can contain a dog and a ball at the same time). For MINST dataset where you have a single label softmax tends to perform better.