吴裕雄--天生自然TensorFlow高层封装：Estimator-自定义模型

# 1. 自定义模型并训练。
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

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

def lenet(x, is_training):
    x = tf.reshape(x, shape=[-1, 28, 28, 1])

    conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)
    conv1 = tf.layers.max_pooling2d(conv1, 2, 2)

    conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)
    conv2 = tf.layers.max_pooling2d(conv2, 2, 2)

    fc1 = tf.contrib.layers.flatten(conv2)
    fc1 = tf.layers.dense(fc1, 1024)
    fc1 = tf.layers.dropout(fc1, rate=0.4, training=is_training)
    return tf.layers.dense(fc1, 10)

def model_fn(features, labels, mode, params):
    predict = lenet(features["image"], mode == tf.estimator.ModeKeys.TRAIN)

    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode,predictions={"result": tf.argmax(predict, 1)})

    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=predict, labels=labels))

    optimizer = tf.train.GradientDescentOptimizer(learning_rate=params["learning_rate"])

    train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())

    eval_metric_ops = {"accuracy": tf.metrics.accuracy(tf.argmax(predict, 1), labels)}

    return tf.estimator.EstimatorSpec(mode=mode,loss=loss,train_op=train_op,eval_metric_ops=eval_metric_ops)

mnist = input_data.read_data_sets("F:\TensorFlowGoogle\201806-github\datasets\MNIST_data", one_hot=False)

model_params = {"learning_rate": 0.01}
estimator = tf.estimator.Estimator(model_fn=model_fn, params=model_params)
  
train_input_fn = tf.estimator.inputs.numpy_input_fn(x={"image": mnist.train.images},y=mnist.train.labels.astype(np.int32),num_epochs=None,batch_size=128,shuffle=True)

estimator.train(input_fn=train_input_fn, steps=30000)

# 2. 在测试数据上测试模型。
test_input_fn = tf.estimator.inputs.numpy_input_fn(x={"image": mnist.test.images},y=mnist.test.labels.astype(np.int32),num_epochs=1,batch_size=128,shuffle=False)

test_results = estimator.evaluate(input_fn=test_input_fn)
accuracy_score = test_results["accuracy"]
print("
Test accuracy: %g %%" % (accuracy_score*100))

# 3. 预测过程。
predict_input_fn = tf.estimator.inputs.numpy_input_fn(x={"image": mnist.test.images[:10]},num_epochs=1,shuffle=False)

predictions = estimator.predict(input_fn=predict_input_fn)
for i, p in enumerate(predictions):
    print("Prediction %s: %s" % (i + 1, p["result"]))