目录
- 1 使用复杂图像
- 2 获取数据
- 3 使用ImageGenerator标记和准备数据¶
- 4 探索数据
- 5 定义模型
- 6 编译模型
- 7 ImageGenerator生成数据
- 8 训练
- 9 测试模型
- 10 可视化中间过程
1 使用复杂图像：马或人
在之前的Fashion MNIST训练图像分类器中。图像时28×28，并且图像居中。本节我们将提升一个新的水平，训练识别图像中的特征，其中主体可以在图像中的任何位置。
2 获取数据
我们将构建马匹和人类的分类来实现上述目的，该分类器将告诉图像是否包含马或人。
https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip
import os
import zipfile
local_zip = "./horse-or-human.zip"
zip_ref = zipfile.ZipFile(local_zip,"r")
zip_ref.extractall("./horse-or-human")
zip_ref.close()
.zip的内容被提取到基本目录horse-or-human中
3 使用ImageGenerator标记和准备数据
在这个样本中我们要注意到一件事是：我们没有明确地将图像标记为马或人。
下面我们将用ImageGenerator来从子目录中读取图像，并从该子目录的名称自动标记它们。
# directory with out trainging horse pictures
train_horse_dir = os.path.join("./horse-or-human/horses")
# dirctory with out training human pictures
train_human_dir = os.path.join("./horse-or-human/humans")
现在，让我们看一下马和人训练目录中的文件名是怎么样的
train_horse_names = os.listdir(train_horse_dir)
print(train_horse_names[:10])
train_human_names = os.listdir(train_human_dir)
print(train_human_names[:10])
['horse01-0.png', 'horse01-1.png', 'horse01-2.png', 'horse01-3.png', 'horse01-4.png', 'horse01-5.png', 'horse01-6.png', 'horse01-7.png', 'horse01-8.png', 'horse01-9.png']
['human01-00.png', 'human01-01.png', 'human01-02.png', 'human01-03.png', 'human01-04.png', 'human01-05.png', 'human01-06.png', 'human01-07.png', 'human01-08.png', 'human01-09.png']
让我们找出目录中马和人类图像的总数：
print("the total training horse images:",len(os.listdir(train_horse_dir)))
print("the total training human images:",len(os.listdir(train_human_dir)))
the total training horse images: 500
the total training human images: 527
4 探索数据
现在我们看看几张照片，以便更好地了解它们的外观。
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.image as mpimg
nrows = 4
ncols = 4
pic_index = 0
现在展示一批8张马和8张人的图片
# Set up matplotlib fig, and size it to fit 4x4 pics
fig = plt.gcf()
fig.set_size_inches(ncols * 4, nrows * 4)
pic_index += 8
next_horse_pix = [os.path.join(train_horse_dir, fname)
for fname in train_horse_names[pic_index-8:pic_index]]
next_human_pix = [os.path.join(train_human_dir, fname)
for fname in train_human_names[pic_index-8:pic_index]]
for i, img_path in enumerate(next_horse_pix+next_human_pix):
# Set up subplot; subplot indices start at 1
sp = plt.subplot(nrows, ncols, i + 1 )
sp.axis('Off') # Don't show axes (or gridlines)
img = mpimg.imread(img_path)
plt.imshow(img)
5 定义模型
第1步导入TensorFlow
import tensorflow as tf
这是一个二分类问题
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(16,(3,3),activation="relu",input_shape=(300,300,3)),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(32,(3,3),activation="relu"),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64,(3,3),activation="relu"),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64,(3,3),activation="relu"),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64,(3,3),activation="relu"),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512,activation="relu"),
tf.keras.layers.Dense(1,activation="sigmoid")
])
WARNING:tensorflow:From D:\software\Anaconda\anaconda\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
model.summary打印模型信息
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 298, 298, 16) 448
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 149, 149, 16) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 147, 147, 32) 4640
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 73, 73, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 71, 71, 64) 18496
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 35, 35, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 33, 33, 64) 36928
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 16, 16, 64) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 14, 14, 64) 36928
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64) 0
_________________________________________________________________
flatten (Flatten) (None, 3136) 0
_________________________________________________________________
dense (Dense) (None, 512) 1606144
_________________________________________________________________
dense_1 (Dense) (None, 1) 513
=================================================================
Total params: 1,704,097
Trainable params: 1,704,097
Non-trainable params: 0
_________________________________________________________________
6 编译模型
from tensorflow.keras.optimizers import RMSprop
model.compile(loss="binary_crossentropy",
optimizer=RMSprop(lr=0.001),
metrics=["acc"])
7 ImageGenerator生成数据
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
"./horse-or-human/",
target_size=(300,300),
batch_size=128,
class_mode="binary"
)
Found 1027 images belonging to 2 classes.
8 训练
history = model.fit_generator(
train_generator,
steps_per_epoch=8,
epochs=15,
verbose=1
)
WARNING:tensorflow:From D:\software\Anaconda\anaconda\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Epoch 1/15
9/9 [==============================] - 85s 9s/step - loss: 0.9717 - acc: 0.5511
Epoch 2/15
9/9 [==============================] - 78s 9s/step - loss: 0.5993 - acc: 0.7527
Epoch 3/15
9/9 [==============================] - 81s 9s/step - loss: 0.7818 - acc: 0.7858
Epoch 4/15
9/9 [==============================] - 82s 9s/step - loss: 0.4991 - acc: 0.8043
Epoch 5/15
9/9 [==============================] - 88s 10s/step - loss: 0.2824 - acc: 0.8939
Epoch 6/15
9/9 [==============================] - 74s 8s/step - loss: 0.1238 - acc: 0.9659
Epoch 7/15
9/9 [==============================] - 83s 9s/step - loss: 0.0556 - acc: 0.9747
Epoch 8/15
9/9 [==============================] - 74s 8s/step - loss: 0.7515 - acc: 0.8822
Epoch 9/15
9/9 [==============================] - 73s 8s/step - loss: 0.0387 - acc: 0.9903
Epoch 10/15
9/9 [==============================] - 74s 8s/step - loss: 0.0553 - acc: 0.9718
Epoch 11/15
9/9 [==============================] - 74s 8s/step - loss: 0.0151 - acc: 0.9961
Epoch 12/15
9/9 [==============================] - 72s 8s/step - loss: 0.0043 - acc: 1.0000
Epoch 13/15
9/9 [==============================] - 75s 8s/step - loss: 0.4870 - acc: 0.8812
Epoch 14/15
9/9 [==============================] - 72s 8s/step - loss: 0.0156 - acc: 0.9961
Epoch 15/15
9/9 [==============================] - 75s 8s/step - loss: 0.0468 - acc: 0.9805
9 测试模型
现在让我们使用该模型实际运行预测。此代码允许你从文件系统中选择一个或多个文件，然后上传它们，并通过模型运行它们，指示对象是马或人
import cv2
import numpy as np
human_img = cv2.imread("human.jpg")
plt.imshow(human_img)
classes = model.predict(human_img.reshape(1,300,300,3))
print(classes[0])
if classes[0] > 0.5:
print("this is a human")
else:
print("this is a horse")
[1.]
this is a human
import cv2
import numpy as np
horse_img = cv2.imread("horse.jpg")
plt.imshow(horse_img)
classes = model.predict(horse_img.reshape(1,300,300,3))
print(classes[0])
if classes[0] > 0.5:
print("this is a human")
else:
print("this is a horse")
[1.]
this is a human
可以看到分辨的话还是不准确的
10 可视化中间过程
从训练集中随机选择一个图像，然后生成一个图像，其中每一行是图层的输出，并且该行输出特征图中的特定 滤镜。
import numpy as np
import random
from tensorflow.keras.preprocessing.image import img_to_array, load_img
# Let's define a new Model that will take an image as input, and will output
# intermediate representations for all layers in the previous model after
# the first.
successive_outputs = [layer.output for layer in model.layers[1:]]
#visualization_model = Model(img_input, successive_outputs)
visualization_model = tf.keras.models.Model(inputs = model.input, outputs = successive_outputs)
# Let's prepare a random input image from the training set.
horse_img_files = [os.path.join(train_horse_dir, f) for f in train_horse_names]
human_img_files = [os.path.join(train_human_dir, f) for f in train_human_names]
img_path = random.choice(horse_img_files + human_img_files)
img = load_img(img_path, target_size=(300, 300)) # this is a PIL image
x = img_to_array(img) # Numpy array with shape (150, 150, 3)
x = x.reshape((1,) + x.shape) # Numpy array with shape (1, 150, 150, 3)
# Rescale by 1/255
x /= 255
# Let's run our image through our network, thus obtaining all
# intermediate representations for this image.
successive_feature_maps = visualization_model.predict(x)
# These are the names of the layers, so can have them as part of our plot
layer_names = [layer.name for layer in model.layers]
# Now let's display our representations
for layer_name, feature_map in zip(layer_names, successive_feature_maps):
if len(feature_map.shape) == 4:
# Just do this for the conv / maxpool layers, not the fully-connected layers
n_features = feature_map.shape[-1] # number of features in feature map
# The feature map has shape (1, size, size, n_features)
size = feature_map.shape[1]
# We will tile our images in this matrix
display_grid = np.zeros((size, size * n_features))
for i in range(n_features):
# Postprocess the feature to make it visually palatable
x = feature_map[0, :, :, i]
x -= x.mean()
x /= x.std()
x *= 64
x += 128
x = np.clip(x, 0, 255).astype('uint8')
# We'll tile each filter into this big horizontal grid
display_grid[:, i * size : (i + 1) * size] = x
# Display the grid
scale = 20. / n_features
plt.figure(figsize=(scale * n_features, scale))
plt.title(layer_name)
plt.grid(False)
plt.imshow(display_grid, aspect='auto', cmap='viridis')
D:\software\Anaconda\anaconda\lib\site-packages\ipykernel_launcher.py:43: RuntimeWarning: invalid value encountered in true_divide
