TensorFlow学习笔记8：CNN搭建(layer,estimator等）

同样的，学习一下用layer等API来搭建CNN。

代码来源：https://github.com/aymericdamien/TensorFlow-Examples

首先，设置相关参数。

from __future__ import division, print_function, absolute_import# 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 np# Training Parameterslearning_rate = 0.001num_steps = 2000batch_size = 128# Network Parametersnum_input = 784 # MNIST data input (img shape: 28*28)num_classes = 10 # MNIST total classes (0-9 digits)dropout = 0.75 # Dropout, probability to keep units

接下来，搭建神经网络。

# Create the neural networkdef conv_net(x_dict, n_classes, dropout, reuse, is_training):        # Define a scope for reusing the variables    with tf.variable_scope('ConvNet', reuse=reuse):        # TF Estimator input is a dict, in case of multiple inputs        x = x_dict['images']        # MNIST data input is a 1-D vector of 784 features (28*28 pixels)        # Reshape to match picture format [Height x Width x Channel]        # Tensor input become 4-D: [Batch Size, Height, Width, Channel]        x = tf.reshape(x, shape=[-1, 28, 28, 1])        # Convolution Layer with 32 filters and a kernel size of 5        conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)        # Max Pooling (down-sampling) with strides of 2 and kernel size of 2        conv1 = tf.layers.max_pooling2d(conv1, 2, 2)        # Convolution Layer with 64 filters and a kernel size of 3        conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)        # Max Pooling (down-sampling) with strides of 2 and kernel size of 2        conv2 = tf.layers.max_pooling2d(conv2, 2, 2)        # Flatten the data to a 1-D vector for the fully connected layer        fc1 = tf.contrib.layers.flatten(conv2)        # Fully connected layer (in tf contrib folder for now)        fc1 = tf.layers.dense(fc1, 1024)        # Apply Dropout (if is_training is False, dropout is not applied)        fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)        # Output layer, class prediction        out = tf.layers.dense(fc1, n_classes)    return out

如上述代码，第一个卷积层有32个5*5的filter，池化，第二个卷积层有64个3*3的filter，池化，flatten层转换为1维向量，经过全连接层，最后输出。

接下来，建立Estimator。

def model_fn(features, labels, mode):        # Build the neural network    # Because Dropout have different behavior at training and prediction time, we    # need to create 2 distinct computation graphs that still share the same weights.    logits_train = conv_net(features, num_classes, dropout, reuse=False, is_training=True)    logits_test = conv_net(features, num_classes, dropout, reuse=True, is_training=False)        # Predictions    pred_classes = tf.argmax(logits_test, axis=1)    pred_probas = tf.nn.softmax(logits_test)        # 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_train, labels=tf.cast(labels, dtype=tf.int32)))    optimizer = tf.train.AdamOptimizer(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)

定义了损失函数，进行优化，评估模型的准确率时直接调用了API。

接下来就可以进行训练和评估了。

# 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)# Train the Modelmodel.train(input_fn, steps=num_steps)# 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)