一些基于python的例子，ai测试，图像处理，matlab调用，文件操作.zip

# TensorFlow-Slim TF-Slim is a lightweight library for defining, training and evaluating models in TensorFlow. It enables defining complex networks quickly and concisely while keeping a model's architecture transparent and its hyperparameters explicit. [TOC] ## Teaser As a demonstration of the simplicity of using TF-Slim, compare the simplicity of the code necessary for defining the entire [VGG](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) network using TF-Slim to the lengthy and verbose nature of defining just the first three layers (out of 16) using native tensorflow: ```python{.good} # VGG16 in TF-Slim. def vgg16(inputs): with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], stddev=0.01, weight_decay=0.0005): net = slim.ops.repeat_op(2, inputs, slim.ops.conv2d, 64, [3, 3], scope='conv1') net = slim.ops.max_pool(net, [2, 2], scope='pool1') net = slim.ops.repeat_op(2, net, slim.ops.conv2d, 128, [3, 3], scope='conv2') net = slim.ops.max_pool(net, [2, 2], scope='pool2') net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 256, [3, 3], scope='conv3') net = slim.ops.max_pool(net, [2, 2], scope='pool3') net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv4') net = slim.ops.max_pool(net, [2, 2], scope='pool4') net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv5') net = slim.ops.max_pool(net, [2, 2], scope='pool5') net = slim.ops.flatten(net, scope='flatten5') net = slim.ops.fc(net, 4096, scope='fc6') net = slim.ops.dropout(net, 0.5, scope='dropout6') net = slim.ops.fc(net, 4096, scope='fc7') net = slim.ops.dropout(net, 0.5, scope='dropout7') net = slim.ops.fc(net, 1000, activation=None, scope='fc8') return net ``` ```python{.bad} # Layers 1-3 (out of 16) of VGG16 in native tensorflow. def vgg16(inputs): with tf.name_scope('conv1_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(inputs, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(bias, name=scope) with tf.name_scope('conv1_2') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(bias, name=scope) with tf.name_scope('pool1') pool1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool1') ``` ## Why TF-Slim? TF-Slim offers several advantages over just the built-in tensorflow libraries: * Allows one to define models much more compactly by eliminating boilerplate code. This is accomplished through the use of [argument scoping](./scopes.py) and numerous high level [operations](./ops.py). These tools increase readability and maintainability, reduce the likelihood of an error from copy-and-pasting hyperparameter values and simplifies hyperparameter tuning. * Makes developing models simple by providing commonly used [loss functions](./losses.py) * Provides a concise [definition](./inception_model.py) of [Inception v3](http://arxiv.org/abs/1512.00567) network architecture ready to be used out-of-the-box or subsumed into new models. Additionally TF-Slim was designed with several principles in mind: * The various modules of TF-Slim (scopes, variables, ops, losses) are independent. This flexibility allows users to pick and choose components of TF-Slim completely à la carte. * TF-Slim is written using a Functional Programming style. That means it's super-lightweight and can be used right alongside any of TensorFlow's native operations. * Makes re-using network architectures easy. This allows users to build new networks on top of existing ones as well as fine-tuning pre-trained models on new tasks. ## What are the various components of TF-Slim? TF-Slim is composed of several parts which were designed to exist independently. These include: * [scopes.py](./scopes.py): provides a new scope named `arg_scope` that allows a user to define default arguments for specific operations within that scope. * [variables.py](./variables.py): provides convenience wrappers for variable creation and manipulation. * [ops.py](./ops.py): provides high level operations for building models using tensorflow. * [losses.py](./losses.py): contains commonly used loss functions. ## Defining Models Models can be succinctly defined using TF-Slim by combining its variables, operations and scopes. Each of these elements are defined below. ### Variables Creating [`Variables`](https://www.tensorflow.org/how_tos/variables/index.html) in native tensorflow requires either a predefined value or an initialization mechanism (random, normally distributed). Furthermore, if a variable needs to be created on a specific device, such as a GPU, the specification must be [made explicit](https://www.tensorflow.org/how_tos/using_gpu/index.html). To alleviate the code required for variable creation, TF-Slim provides a set of thin wrapper functions in [variables.py](./variables.py) which allow callers to easily define variables. For example, to create a `weight` variable, initialize it using a truncated normal distribution, regularize it with an `l2_loss` and place it on the `CPU`, one need only declare the following: ```python weights = variables.variable('weights', shape=[10, 10, 3 , 3], initializer=tf.truncated_normal_initializer(stddev=0.1), regularizer=lambda t: losses.l2_loss(t, weight=0.05), device='/cpu:0') ``` In addition to the functionality provided by `tf.Variable`, `slim.variables` keeps track of the variables created by `slim.ops` to define a model, which allows one to distinguish variables that belong to the model versus other variables. ```python # Get all the variables defined by the model. model_variables = slim.variables.get_variables() # Get all the variables with the same given name, i.e. 'weights', 'biases'. weights = slim.variables.get_variables_by_name('weights') biases = slim.variables.get_variables_by_name('biases') # Get all the variables in VARIABLES_TO_RESTORE collection. variables_to_restore = tf.get_collection(slim.variables.VARIABLES_TO_RESTORE) weights = variables.variable('weights', shape=[10, 10, 3 , 3], initializer=tf.truncated_normal_initializer(stddev=0.1), regularizer=lambda t: losses.l2_loss(t, weight=0.05), device='/cpu:0') ``` ### Operations (Layers) While the set of TensorFlow operations is quite extensive, builders of neural networks typically think of models in terms of "layers". A layer, such as a Convolutional Layer, a Fully Connected Layer or a BatchNorm Layer are more abstract than a single TensorFlow operation and typically involve many such operations. For example, a Convolutional Layer in a neural network is built using several steps: 1. Creating the weight variables 2. Creating the bias variables 3. Convolving the weights with the input from the previous layer 4. Adding the biases to the result of the convolution. In python code this can be rather laborious: ```python input = ... with tf.name_scope('conv1_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(input, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant