code-04-DefineAccuracy.py 文件源码

def main():
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

    # Placeholder that will be fed image data.
    x = tf.placeholder(tf.float32, [None, 784])
    # Placeholder that will be fed the correct labels.
    y_ = tf.placeholder(tf.float32, [None, 10])

    # Define weight and bias.
    W = weight_variable([784, 10])
    b = bias_variable([10])

    # Here we define our model which utilizes the softmax regression.
    y = tf.nn.softmax(tf.matmul(x, W) + b)

    # Define our loss.
    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

    # Define our optimizer.
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

    # Define accuracy.
    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
    correct_prediction = tf.cast(correct_prediction, tf.float32)
    accuracy = tf.reduce_mean(correct_prediction)