python用TensorFlow做图像识别的实现

from tensorflow.examples.tutorials.mnist import input_data
MNIST = input_data.read_data_sets("/data/mnist", one_hot=True)

X = tf.placeholder(tf.float32, [batch_size, 784], name="image")
Y = tf.placeholder(tf.float32, [batch_size, 10], name="label")

w = tf.Variable(tf.random_normal(shape=[784, 10], stddev=0.01), name="weights")
b = tf.Variable(tf.zeros([1, 10]), name="bias")

logits = tf.matmul(X, w) + b
entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y)
loss = tf.reduce_sum(entropy)

optimizer =
tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
  sess.run(init)
  n_batches = int(MNIST.train.num_examples/batch_size)
  for i in range(n_epochs): # train the model n_epochs times
    for _ in range(n_batches):
      X_batch, Y_batch = MNIST.train.next_batch(batch_size)
      sess.run([optimizer, loss], feed_dict={X: X_batch, Y:Y_batch})

n_batches = int(MNIST.test.num_examples/batch_size)
  total_correct_preds = 0
  for i in range(n_batches):
    X_batch, Y_batch = MNIST.test.next_batch(batch_size)
    _, loss_batch, logits_batch = sess.run([optimizer, loss, logits],
    feed_dict={X: X_batch, Y:Y_batch})
    preds = tf.nn.softmax(logits_batch)
    correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(Y_batch, 1))
    accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32))
    total_correct_preds += sess.run(accuracy)
  print ("Accuracy {0}".format(total_correct_preds/MNIST.test.num_examples))