TensorFlow学习笔记6:神经网络搭建(layer,estimator等)

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这次来看一看如何用layer等API来搭建神经网络。

from __future__ import print_function# Import MNIST datafrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=False)import tensorflow as tfimport matplotlib.pyplot as pltimport numpy as nplearning_rate = 0.1num_steps = 1000batch_size = 128display_step = 100# Network Parametersn_hidden_1 = 256 # 1st layer number of neuronsn_hidden_2 = 256 # 2nd layer number of neuronsnum_input = 784 # MNIST data input (img shape: 28*28)num_classes = 10 # MNIST total classes (0-9 digits)# Define the input function for traininginput_fn = tf.estimator.inputs.numpy_input_fn(    x={'images': mnist.train.images}, y=mnist.train.labels,    batch_size=batch_size, num_epochs=None, shuffle=True)
基本的参数设置和上一篇博客中没有区别。

这里,输入函数numpy_input_fn(x, y=None, batch_size=128, num_epochs=1, shuffle=None, queue_capacity=1000, num_threads=1) 中x必须为一个字典,y为数组或者None。

接下来定义神经网络。

# Define the neural networkdef neural_net(x_dict):    # TF Estimator input is a dict, in case of multiple inputs    x = x_dict['images']    # Hidden fully connected layer with 256 neurons    layer_1 = tf.layers.dense(x, n_hidden_1)    # Hidden fully connected layer with 256 neurons    layer_2 = tf.layers.dense(layer_1, n_hidden_2)    # Output fully connected layer with a neuron for each class    out_layer = tf.layers.dense(layer_2, num_classes)    return out_layer
可以看到,与上一篇博客不同,这里用tf.layers.dense 直接建立了全连接层。

接下来,建立estimator。先定义model_fn, 建立神经网络,定义损失函数,优化,并且进行相关的评估工作。

model = tf.estimator.Estimator(model_fn) 可以建立estimator。

# Define the model function (following TF Estimator Template)def model_fn(features, labels, mode):        # Build the neural network    logits = neural_net(features)        # Predictions    pred_classes = tf.argmax(logits, axis=1)    pred_probas = tf.nn.softmax(logits)        # If prediction mode, early return    if mode == tf.estimator.ModeKeys.PREDICT:        return tf.estimator.EstimatorSpec(mode, predictions=pred_classes)             # Define loss and optimizer    loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(        logits=logits, labels=tf.cast(labels, dtype=tf.int32)))    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)    train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())        # Evaluate the accuracy of the model    acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)        # TF Estimators requires to return a EstimatorSpec, that specify    # the different ops for training, evaluating, ...    estim_specs = tf.estimator.EstimatorSpec(      mode=mode,      predictions=pred_classes,      loss=loss_op,      train_op=train_op,      eval_metric_ops={'accuracy': acc_op})    return estim_specs
# Build the Estimatormodel = tf.estimator.Estimator(model_fn)
开始训练:

# Train the Modelmodel.train(input_fn, steps=num_steps)
对训练好的模型进行评估:

# Evaluate the Model# Define the input function for evaluatinginput_fn = tf.estimator.inputs.numpy_input_fn(    x={'images': mnist.test.images}, y=mnist.test.labels,    batch_size=batch_size, shuffle=False)# Use the Estimator 'evaluate' methodmodel.evaluate(input_fn)





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