TensorFlow (七): Estimator

矫情吗；* 2022-03-09 00:42 220阅读 0赞

### Estimator ###

*  简介
 *  代码
 *  自定义 Model

# 简介 #

之前用 TensorFlow 写的神经网络都比较裸, 这次介绍的 Estimator 则是对神经网络的一种封装.

# 代码 #

import tensorflow as tf 
    import numpy as np 
    import tensorflow.contrib.slim as slim 
    import mnist_loader as ml 
    from tensorflow.estimator import DNNClassifier
    from tensorflow.estimator.inputs import numpy_input_fn
    
    
    x_train, y_train = ml.load_mnist("./MNIST_DATA", "train")
    x_test, y_test = ml.load_mnist("./MNIST_DATA", "t10k")
    
    x_train = x_train / 255.0
    x_test = x_test / 255.0
    y_train = y_train.astype(np.int32)
    y_test = y_test.astype(np.int32)
    
    train_input_fn = numpy_input_fn(
    				 				x = { "x": x_train}, 
    				 				y = y_train,
    				 				shuffle = True,
    				 				num_epochs = None)
    
    test_input_fn = numpy_input_fn(
    							    x = { "x": x_test},
    							    y = y_test,
    							    shuffle = False)
    
    pred_input_fn = numpy_input_fn(
    								x = { "x": x_test[:100]},
    								shuffle = False)
    
    feature_x = tf.feature_column.numeric_column("x", shape = [784])
    feature_columns = [feature_x]
    num_hidden_units = [64, 32]
    
    model = DNNClassifier(
    					  feature_columns = feature_columns,
    					  hidden_units = num_hidden_units,
    					  activation_fn = tf.nn.relu,
    					  n_classes = 10)
    
    model.train(input_fn = train_input_fn, steps = 2000)
    result = model.evaluate(input_fn = test_input_fn)
    print(result)
    predictions = model.predict(input_fn = pred_input_fn)
    cls = [p["classes"] for p in predictions]
    cls = np.array(cls, dtype = np.int32).squeeze()
    accuracy = np.mean(np.equal(cls, y_test[:100]).astype(np.int32))
    print(accuracy)

可以看到其中一个变化是, 所有的输入都变成了一个 input\_function, 这样做的好处是将原本的数组形式变成了一种类似于迭代器的形式, 从而为下一篇要讲到的 TFRecorder 提供了编程基础.  
还有一个变化是, 神经网络被封装了, 在这个代码中神经网络使用的是官方的API, 在下文将介绍如何使用自己的逻辑构建神经网络.

# 自定义 Model #

import tensorflow as tf 
    import numpy as np 
    import tensorflow.contrib.slim as slim 
    import mnist_loader as ml 
    from tensorflow.estimator.inputs import numpy_input_fn
    
    def model_fn(features, labels, mode, params = None):
    	x = features["x"]
    
    	net = tf.reshape(x, [-1, 28, 28, 1])
    	net = slim.conv2d(net, 16, [3, 3])
    	net = slim.flatten(net)
    	net = slim.fully_connected(net, 32)
    	logits = slim.fully_connected(net, 10, activation_fn = None)
    	y = tf.nn.softmax(logits)
    	y_cls = tf.argmax(y, axis = 1)
    
    	if mode == tf.estimator.ModeKeys.PREDICT:
    		return tf.estimator.EstimatorSpec(mode = mode, predictions = y_cls)
    	else:
    		loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits = logits, labels = labels))
    		optimizer = tf.train.AdamOptimizer(0.1)
    		train_op = optimizer.minimize(loss, global_step = tf.train.get_global_step())
    		metrics = { 
    			"accuracy": tf.metrics.accuracy(y_cls, labels)
    		}
    		spec = tf.estimator.EstimatorSpec(
    											mode = mode,
    											loss = loss,
    											train_op = train_op,
    											eval_metric_ops = metrics)
    		return spec
    
    x_train, y_train = ml.load_mnist("./MNIST_DATA", "train")
    x_test, y_test = ml.load_mnist("./MNIST_DATA", "t10k")
    
    x_train = x_train / 255.0
    x_test = x_test / 255.0
    y_train = y_train.astype(np.int32)
    y_test = y_test.astype(np.int32)
    
    train_input_fn = numpy_input_fn(
    				 				x = { "x": x_train}, 
    				 				y = y_train,
    				 				shuffle = True,
    				 				num_epochs = None)
    
    test_input_fn = numpy_input_fn(
    							    x = { "x": x_test},
    							    y = y_test,
    							    shuffle = False)
    
    pred_input_fn = numpy_input_fn(
    								x = { "x": x_test[:100]},
    								shuffle = False)
    
    model = tf.estimator.Estimator(model_fn = model_fn)
    
    model.train(input_fn = train_input_fn, steps = 100)
    result = model.evaluate(input_fn = test_input_fn)
    print(result)
    predictions = model.predict(input_fn = pred_input_fn)
    # cls = [p["classes"] for p in predictions]
    cls = np.array(list(predictions), dtype = np.int32).squeeze()
    accuracy = np.mean(np.equal(cls, y_test[:100]).astype(np.int32))
    print(accuracy)