dlnd_image_classification.py

# coding: utf-8

# # 图像分类
# 
# 在此项目中，你将对 [CIFAR-10 数据集](https://www.cs.toronto.edu/~kriz/cifar.html) 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。
# 
# 
# ## 获取数据
# 
# 请运行以下单元，以下载 [CIFAR-10 数据集（Python版）](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)。
# 

# In[1]:

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile

cifar10_dataset_folder_path = 'cifar-10-batches-py'

# Use Floyd's cifar-10 dataset if present
floyd_cifar10_location = '/input/cifar-10/python.tar.gz'
if isfile(floyd_cifar10_location):
    tar_gz_path = floyd_cifar10_location
else:
    tar_gz_path = 'cifar-10-python.tar.gz'

class DLProgress(tqdm):
    last_block = 0

    def hook(self, block_num=1, block_size=1, total_size=None):
        self.total = total_size
        self.update((block_num - self.last_block) * block_size)
        self.last_block = block_num

if not isfile(tar_gz_path):
    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:
        urlretrieve(
            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
            tar_gz_path,
            pbar.hook)

if not isdir(cifar10_dataset_folder_path):
    with tarfile.open(tar_gz_path) as tar:
        tar.extractall()
        tar.close()


tests.test_folder_path(cifar10_dataset_folder_path)


# ## 探索数据
# 
# 该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 `data_batch_1`、`data_batch_2`，以此类推。每个部分都包含以下某个类别的标签和图片：
# 
# * 飞机
# * 汽车
# * 鸟类
# * 猫
# * 鹿
# * 狗
# * 青蛙
# * 马
# * 船只
# * 卡车
# 
# 了解数据集也是对数据进行预测的必经步骤。你可以通过更改 `batch_id` 和 `sample_id` 探索下面的代码单元。`batch_id` 是数据集一个部分的 ID（1 到 5）。`sample_id` 是该部分中图片和标签对（label pair）的 ID。
# 
# 问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。
# 

# In[2]:

get_ipython().magic('matplotlib inline')
get_ipython().magic("config InlineBackend.figure_format = 'retina'")

import helper
import numpy as np

# Explore the dataset
batch_id = 1
sample_id = 5
helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)


# ## 实现预处理函数
# 
# ### 标准化
# 
# 在下面的单元中，实现 `normalize` 函数，传入图片数据 `x`，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 `x` 的形状一样。
# 

# In[3]:

def normalize(x):
    """
    Normalize a list of sample image data in the range of 0 to 1
    : x: List of image data.  The image shape is (32, 32, 3)
    : return: Numpy array of normalize data
    """
    # TODO: Implement Function
    try:
        x_normalize = (x - x.min()) / (x.max() - x.min())
    except:
        x_normalize = x
    return np.array(x_normalize)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_normalize(normalize)


# ### One-hot 编码
# 
# 和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 `one_hot_encode` 函数。输入，也就是 `x`，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 `one_hot_encode` 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。
# 
# 提示：不要重复发明轮子。
# 

# In[4]:

def one_hot_encode(x):
    """
    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
    : x: List of sample Labels
    : return: Numpy array of one-hot encoded labels
    """
    # TODO: Implement Function
    return np.eye(10)[x]


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_one_hot_encode(one_hot_encode)


# ### 随机化数据
# 
# 之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。
# 

# ## 预处理所有数据并保存
# 
# 运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。
# 

# In[5]:

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)


# # 检查点
# 
# 这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。
# 

# In[6]:

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import pickle
import problem_unittests as tests
import helper

# Load the Preprocessed Validation data
valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))


# ## 构建网络
# 
# 对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。
# 
# >**注意**：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。
# 
# >但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 `conv2d` 类 [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d)，而不是 TF Layers 版本的 `conv2d` 类 [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d)。
# 
# 我们开始吧！
# 
# 
# ### 输入
# 
# 神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：
# 
# * 实现 `neural_net_image_input`
#  * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)
#  * 使用 `image_shape` 设置形状，部分大小设为 `None`
#  * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 "x" 命名
# * 实现 `neural_net_label_input`
#  * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)
#  * 使用 `n_classes` 设置形状，部分大小设为 `None`
#  * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 "y" 命名
# * 实现 `neural_net_keep_prob_input`
#  * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)，用于丢弃保留概率
#  * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 "keep_prob" 命名
# 
# 这些名称将在项目结束时，用于加载保存的模型。
# 
# 注意：TensorFlow 中的 `None` 表示形状可以是动态大小。

# In[7]:

import tensorflow as tf

def neural_net_image_input(image_shape):
    """
    Return a Tensor for a batch of image input
    : image_shape: Shape of the images
    : return: Tensor for image input.
    """
    # TODO: Implement Function
    shape = (None, *image_shape)
    x = tf.placeholder(tf.float32, shape, name='x')
    return x


def neural_net_label_input(n_classes):
    """
    Return a Tensor for a batch of label input
    : n_classes: Number of classes
    : return: Tensor for label input.
    """
    # TODO: Implement Function
    shape = [None, n_classes]
    y = tf.placeholder(tf.float32, shape, name='y')
    return y


def neural_net_keep_prob_input():
    """
    Return a Tensor for keep probability
    : return: Tensor for keep probability.
    """
    # TODO: Implement Function
    prob = tf.placeholder(tf.float32, None, name='keep_prob')
    return prob


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tf.reset_default_graph()
tests.test_nn_image_inputs(neural_net_image_input)
tests.test_nn_label_inputs(neural_net_label_input)
tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)


# ### 卷积和最大池化层
# 
# 卷积层级适合处理图片。对于此代码单元，你应该实现函数 `conv2d_maxpool` 以便应用卷积然后进行最大池化：
# 
# * 使用 `conv_ksize`、`conv_num_outputs` 和 `x_tensor` 的形状创建权重（weight）和偏置（bias）。
# * 使用权重和 `conv_strides` 对 `x_tensor` 应用卷积。
#  * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。
# * 添加偏置
# * 向卷积中添加非线性激活（nonlinear activation）
# * 使用 `pool_ksize` 和 `pool_strides` 应用最大池化
#  * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。
# 
# **注意**：对于**此层**，**请勿使用** [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers)，但是仍然可以使用 TensorFlow 的 [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) 包。对于所有**其他层**，你依然可以使用快捷方法。
# 

# In[8]:

def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):
    """
    Apply convolution then max pooling to x_tensor
    :param x_tensor: TensorFlow Tensor
    :param conv_num_outputs: Number of outputs for the convolutional layer
    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer
    :param conv_strides: Stride 2-D Tuple for convolution
    :param pool_ksize: kernal size 2-D Tuple for pool
    :param pool_strides: Stride 2-D Tuple for pool
    : return: A tensor that represents convolution and max pooling of x_tensor
    """
    # TODO: Implement Function
    channel = x_tensor.get_shape().as_list()[-1]
    w = tf.Variable(tf.truncated_normal([conv_ksize[0], conv_ksize[1], channel, conv_num_outputs], stddev=0.1))
    b = tf.Variable(tf.zeros([conv_num_outputs]))
    x_tensor = tf.nn.conv2d(x_tensor, w, strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME')
    x_tensor = tf.nn.bias_add(x_tensor, b)
    x_tensor = tf.nn.relu(x_tensor)
    x_tensor = tf.nn.max_pool(
        x_tensor,
        ksize=[1, pool_ksize[0], pool_ksize[1], 1],
        strides=[1, pool_strides[0], pool_strides[1], 1],
        padding='SAME')
    
    return x_tensor 


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_con_pool(conv2d_maxpool)


# ### 扁平化层
# 
# 实现 `flatten` 函数，将 `x_tensor` 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（*部分大小（Batch Size）*，*扁平化图片大小（Flattened Image Size）*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。
# 

# In[9]:

def flatten(x_tensor):
    """
    Flatten x_tensor to (Batch Size, Flattened Image Size)
    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
    : return: A tensor of size (Batch Size, Flattened Image Size).
    """
    # TODO: Implement Function
    return tf.contrib.layers.flatten(x_tensor)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_flatten(flatten)


# ### 全连接层
# 
# 实现 `fully_conn` 函数，以向 `x_tensor` 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。

# In[10]:

def fully_conn(x_tensor, num_outputs):
    """
    Apply a fully connected layer to x_tensor using weight and bias
    : x_tensor: A 2-D tensor where the first dimension is batch size.
    : num_outputs: The number of output that the new tensor should be.
    : return: A 2-D tensor where the second dimension is num_outputs.
    """
    # TODO: Implement Function
    channel = x_tensor.get_shape().as_list()[-1]
    w = tf.Variable(tf.truncated_normal([channel, num_outputs], stddev=0.1))
    b = tf.Variable(tf.zeros([num_outputs]))
    x_tensor = tf.add(tf.matmul(x_tensor, w), b)
    x_tensor = tf.nn.relu(x_tensor)
    
    return x_tensor


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_fully_conn(fully_conn)


# ### 输出层
# 
# 实现 `output` 函数，向 x_tensor 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。
# 
# **注意**：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。

# In[11]:

def output(x_tensor, num_outputs):
    """
    Apply a output layer to x_tensor using weight and bias
    : x_tensor: A 2-D tensor where the first dimension is batch size.
    : num_outputs: The number of output that the new tensor should be.
    : return: A 2-D tensor where the second dimension is num_outputs.
    """
    # TODO: Implement Function
    channel = x_tensor.get_shape().as_list()[1]
    w = tf.Variable(tf.truncated_normal([channel, num_outputs], stddev=0.1))
    b = tf.Variable(tf.zeros([num_outputs]))
    x_tensor = tf.add(tf.matmul(x_tensor, w), b)
    
    return x_tensor


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_output(output)


# ### 创建卷积模型
# 
# 实现函数 `conv_net`， 创建卷积神经网络模型。该函数传入一批图片 `x`，并输出对数（logits）。使用你在上方创建的层创建此模型：
# 
# * 应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）
# * 应用一个扁平层（Flatten Layer）
# * 应用 1、2 或 3 个完全连接层（Fully Connected Layers）
# * 应用一个输出层（Output Layer）
# * 返回输出
# * 使用 `keep_prob` 向模型中的一个或多个层应用 [TensorFlow 的 Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout)

# In[12]:

def conv_net(x, keep_prob):
    """
    Create a convolutional neural network model
    : x: Placeholder tensor that holds image data.
    : keep_prob: Placeholder tensor that hold dropout keep probability.
    : return: Tensor that represents logits
    """
    conv_ksize = (3,3)
    conv_strides = (1,1)
    pool_ksize = (2,2)
    pool_strides = (2,2)
    num_outputs = 10
    
    # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers
    #    Play around with different number of outputs, kernel size and stride
    # Function Definition from Above:
    #    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)
    c1 = conv2d_maxpool(x, 16, conv_ksize, conv_strides, pool_ksize, pool_strides)
    c2 = conv2d_maxpool(c1, 32, conv_ksize, conv_strides, pool_ksize, pool_strides)
    c3 = conv2d_maxpool(c2, 64, conv_ksize, conv_strides, pool_ksize, pool_strides)
    
    # TODO: Apply a Flatten Layer
    # Function Definition from Above:
    #   flatten(x_tensor)
    f0 = flatten(c3)

    # TODO: Apply 1, 2, or 3 Fully Connected Layers
    #    Play around with different number of outputs
    # Function Definition from Above:
    #   fully_conn(x_tensor, num_outputs)
    f1 = fully_conn(f0, 128)
    f1 = tf.nn.dropout(f1, keep_prob)
    f2 = fully_conn(f1, 64)
    
    # TODO: Apply an Output Layer
    #    Set this to the number of classes
    # Function Definition from Above:
    #   output(x_tensor, num_outputs)
    out = output(f2, num_outputs)
    
    # TODO: return output
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""

##############################
## Build the Neural Network ##
##############################

# Remove previous weights, bias, inputs, etc..
tf.reset_default_graph()

# Inputs
x = neural_net_image_input((32, 32, 3))
y = neural_net_label_input(10)
keep_prob = neural_net_keep_prob_input()

# Model
logits = conv_net(x, keep_prob)

# Name logits Tensor, so that is can be loaded from disk after training
logits = tf.identity(logits, name='logits')

# Loss and Optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)

# Accuracy
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')

tests.test_conv_net(conv_net)


# ## 训练神经网络
# 
# ### 单次优化
# 
# 实现函数 `train_neural_network` 以进行单次优化（single optimization）。该优化应该使用 `optimizer` 优化 `session`，其中 `feed_dict` 具有以下参数：
# 
# * `x` 表示图片输入
# * `y` 表示标签
# * `keep_prob` 表示丢弃的保留率
# 
# 每个部分都会调用该函数，所以 `tf.global_variables_initializer()` 已经被调用。
# 
# 注意：不需要返回任何内容。该函数只是用来优化神经网络。
# 

# In[13]:

def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):
    """
    Optimize the session on a batch of images and labels
    : session: Current TensorFlow session
    : optimizer: TensorFlow optimizer function
    : keep_probability: keep probability
    : feature_batch: Batch of Numpy image data
    : label_batch: Batch of Numpy label data
    """
    # TODO: Implement Function
    session.run(optimizer, feed_dict={x: feature_batch, y: label_batch, keep_prob: keep_probability})


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_train_nn(train_neural_network)


# ### 显示数据
# 
# 实现函数 `print_stats` 以输出损失和验证准确率。使用全局变量 `valid_features` 和 `valid_labels` 计算验证准确率。使用保留率 `1.0` 计算损失和验证准确率（loss and validation accuracy）。
# 

# In[14]:

def print_stats(session, feature_batch, label_batch, cost, accuracy):
    """
    Print information about loss and validation accuracy
    : session: Current TensorFlow session
    : feature_batch: Batch of Numpy image data
    : label_batch: Batch of Numpy label data
    : cost: TensorFlow cost function
    : accuracy: TensorFlow accuracy function
    """
    # TODO: Implement Function
    loss = session.run(cost, feed_dict={x: feature_batch, y: label_batch, keep_prob: 1.})
    valid_acc = session.run(accuracy, feed_dict={x: valid_features, y: valid_labels, keep_prob: 1.})
    print('Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(loss, valid_acc))


# ### 超参数
# 
# 调试以下超参数：
# * 设置 `epochs` 表示神经网络停止学习或开始过拟合的迭代次数
# * 设置 `batch_size`，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：
# 
#  * 64
#  * 128
#  * 256
#  * ...
# * 设置 `keep_probability` 表示使用丢弃时保留节点的概率

# In[17]:

# TODO: Tune Parameters
epochs = 100
batch_size = 128
keep_probability = 0.5


# ### 在单个 CIFAR-10 部分上训练
# 
# 我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。
# 

# In[18]:

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
print('Checking the Training on a Single Batch...')
with tf.Session() as sess:
    # Initializing the variables
    sess.run(tf.global_variables_initializer())
    
    # Training cycle
    for epoch in range(epochs):
        batch_i = 1
        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')
        print_stats(sess, batch_features, batch_labels, cost, accuracy)


# ### 完全训练模型
# 
# 现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。

# In[19]:

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
save_model_path = './image_classification'

print('Training...')
with tf.Session() as sess:
    # Initializing the variables
    sess.run(tf.global_variables_initializer())
    
    # Training cycle
    for epoch in range(epochs):
        # Loop over all batches
        n_batches = 5
        for batch_i in range(1, n_batches + 1):
            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')
            print_stats(sess, batch_features, batch_labels, cost, accuracy)
            
    # Save Model
    saver = tf.train.Saver()
    save_path = saver.save(sess, save_model_path)


# # 检查点
# 
# 模型已保存到本地。
# 
# ## 测试模型
# 
# 利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。

# In[23]:

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
get_ipython().magic('matplotlib inline')
get_ipython().magic("config InlineBackend.figure_format = 'retina'")

import tensorflow as tf
import pickle
import helper
import random

# Set batch size if not already set
try:
    if batch_size:
        pass
except NameError:
    batch_size = 64

save_model_path = './image_classification'
n_samples = 4
top_n_predictions = 3

def test_model():
    """
    Test the saved model against the test dataset
    """

    test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))
    loaded_graph = tf.Graph()

    with tf.Session(graph=loaded_graph) as sess:
        # Load model
        loader = tf.train.import_meta_graph(save_model_path + '.meta')
        loader.restore(sess, save_model_path)

        # Get Tensors from loaded model
        loaded_x = loaded_graph.get_tensor_by_name('x:0')
        loaded_y = loaded_graph.get_tensor_by_name('y:0')
        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
        
        # Get accuracy in batches for memory limitations
        test_batch_acc_total = 0
        test_batch_count = 0
        
        for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
            test_batch_acc_total += sess.run(
                loaded_acc,
                feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})
            test_batch_count += 1

        print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))

        # Print Random Samples
        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
        random_test_predictions = sess.run(
            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})
        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)


test_model()


# ## 为何准确率只有50-80%？
# 
# 你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率[远远超过 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130)。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。
# 
# ## 提交项目
# 
# 提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 "File" -> "Download as" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。
#