In [1]:
from tensorflow import keras import tensorflow as tf import numpy as np import pandas as pd import matplotlib.pyplot as pltIn [2]:
# 文件下载地址 https://www.kaggle.com/datasets/slothkong/10-monkey-species train_dir = './10-monkey-species/training/training' valid_dir = './10-monkey-species/validation/validation' label_file = './10-monkey-species/monkey_labels.txt'In [3]:
df = pd.read_csv(label_file, header=0) df
| Label | Latin Name | Common Name | Train Images | Validation Images | |
|---|---|---|---|---|---|
| 0 | n0 | alouatta_palliata\t | mantled_howler | 131 | 26 |
| 1 | n1 | erythrocebus_patas\t | patas_monkey | 139 | 28 |
| 2 | n2 | cacajao_calvus\t | bald_uakari | 137 | 27 |
| 3 | n3 | macaca_fuscata\t | japanese_macaque | 152 | 30 |
| 4 | n4 | cebuella_pygmea\t | pygmy_marmoset | 131 | 26 |
| 5 | n5 | cebus_capucinus\t | white_headed_capuchin | 141 | 28 |
| 6 | n6 | mico_argentatus\t | silvery_marmoset | 132 | 26 |
| 7 | n7 | saimiri_sciureus\t | common_squirrel_monkey | 142 | 28 |
| 8 | n8 | aotus_nigriceps\t | black_headed_night_monkey | 133 | 27 |
| 9 | n9 | trachypithecus_johnii | nilgiri_langur | 132 | 26 |
# 图片数据生成器
height = 128
width = 128
channels = 3
batch_size =32
num_classes = 10
train_datagen = keras.preprocessing.image.ImageDataGenerator(
rescale = 1. / 255, # 归一化&浮点数
rotation_range = 40, # 随机旋转 0~40°之间
width_shift_range = 0.2, # 随机水平移动
height_shift_range = 0.2, # 随机垂直移动
shear_range = 0.2, # 随机裁剪比例
zoom_range = 0.2, # 随机缩放比例
horizontal_flip = True, # 随机水平翻转
vertical_flip = True, # 随机垂直翻转
fill_mode = 'nearest', # 填充模式
)
train_generator = train_datagen.flow_from_directory(train_dir, target_size=(height, width),
batch_size=batch_size, shuffle=True, class_mode='categorical')
valid_datagen = keras.preprocessing.image.ImageDataGenerator(
rescale = 1. / 255, # 归一化&浮点数
)
valid_generator = valid_datagen.flow_from_directory(valid_dir, target_size=(height, width),
batch_size=batch_size, shuffle=False, class_mode='categorical')
Found 1098 images belonging to 10 classes. Found 272 images belonging to 10 classes.In [5]:
x, y = train_generator.next() # 可以看出 y是经过one hot编码的 print(x.shape, y.shape)
(32, 128, 128, 3) (32, 10)In [6]:
model = tf.keras.models.Sequential() # 卷积 # input_shape 输入参数为 (height, width, channels) model.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = 3, padding = 'same', activation = 'selu', input_shape = (height, width, 3))) model.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = 3, padding = 'same', activation = 'selu')) # 卷积 model.add(tf.keras.layers.MaxPool2D()) # 池化 model.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = 3, padding = 'same', activation = 'selu')) # 卷积 model.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = 3, padding = 'same', activation = 'selu')) # 卷积 model.add(tf.keras.layers.MaxPool2D()) # 池化 model.add(tf.keras.layers.Conv2D(filters = 128, kernel_size = 3, padding = 'same', activation = 'selu')) # 卷积 model.add(tf.keras.layers.Conv2D(filters = 128, kernel_size = 3, padding = 'same', activation = 'selu')) # 卷积 model.add(tf.keras.layers.MaxPool2D()) # 池化 model.add(tf.keras.layers.Conv2D(filters = 256, kernel_size = 3, padding = 'same', activation = 'selu')) # 卷积 model.add(tf.keras.layers.Conv2D(filters = 256, kernel_size = 3, padding = 'same', activation = 'selu')) # 卷积 model.add(tf.keras.layers.MaxPool2D()) # 池化 # 输入输出shape: 具体而言，是将一个维度大于或等于3的高维矩阵，“压扁”为一个二维矩阵。即保留第一个维度（如：batch的个数）， # 然后将剩下维度的值相乘为“压扁”矩阵的第二个维度。如输入是(None, 32，32，3)，则输出是(None, 3072) model.add(tf.keras.layers.Flatten()) # Flatten层用来将输入“压平”，常用在从卷积层到全连接层的过渡。Flatten不影响batch的大小。 model.add(tf.keras.layers.Dense(2048, activation='selu')) model.add(tf.keras.layers.AlphaDropout(0.3)) model.add(tf.keras.layers.Dense(1024, activation='selu')) model.add(tf.keras.layers.AlphaDropout(0.25)) model.add(tf.keras.layers.Dense(512, activation='selu')) model.add(tf.keras.layers.Dense(128, activation='selu')) model.add(tf.keras.layers.Dense(10, activation='softmax')) # 配置网络 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])In [7]:
# 训练
train_num = train_generator.samples
valid_num = valid_generator.samples
history = model.fit(train_generator, steps_per_epoch=train_num//batch_size, epochs=10,
validation_data=valid_generator, validation_steps=valid_num//batch_size)
Epoch 1/10 34/34 [==============================] - 42s 1s/step - loss: 18.0979 - acc: 0.1285 - val_loss: 4.8664 - val_acc: 0.1562 Epoch 2/10 34/34 [==============================] - 33s 957ms/step - loss: 2.6509 - acc: 0.1754 - val_loss: 2.7836 - val_acc: 0.2188 Epoch 3/10 34/34 [==============================] - 31s 921ms/step - loss: 2.2543 - acc: 0.2073 - val_loss: 3.4844 - val_acc: 0.1172 Epoch 4/10 34/34 [==============================] - 31s 900ms/step - loss: 2.0929 - acc: 0.2514 - val_loss: 2.6293 - val_acc: 0.2617 Epoch 5/10 34/34 [==============================] - 31s 917ms/step - loss: 2.0205 - acc: 0.2711 - val_loss: 2.4111 - val_acc: 0.2461 Epoch 6/10 34/34 [==============================] - 31s 919ms/step - loss: 1.9451 - acc: 0.3002 - val_loss: 2.5277 - val_acc: 0.2852 Epoch 7/10 34/34 [==============================] - 32s 924ms/step - loss: 1.8503 - acc: 0.3283 - val_loss: 2.5161 - val_acc: 0.3125 Epoch 8/10 34/34 [==============================] - 30s 882ms/step - loss: 1.8364 - acc: 0.3386 - val_loss: 2.5492 - val_acc: 0.2656 Epoch 9/10 34/34 [==============================] - 30s 867ms/step - loss: 1.7762 - acc: 0.3940 - val_loss: 1.8829 - val_acc: 0.3750 Epoch 10/10 34/34 [==============================] - 30s 873ms/step - loss: 1.7863 - acc: 0.3687 - val_loss: 2.1468 - val_acc: 0.3359In [16]:
pd.DataFrame(history.history).plot(figsize=(8, 5)) plt.grid() plt.gca().set_ylim(0, 10) plt.show()
In [13]:
model.evaluate(valid_generator) # 可以看出效果很差，还是需要用RestNet这样的复杂网络才可以
9/9 [==============================] - 3s 297ms/step - loss: 2.0539 - acc: 0.3640Out[13]:
[2.053945541381836, 0.3639705777168274]标签：acc,10,monkey,keras,loss,tf,TensorFlow,model From： https://www.cnblogs.com/funsion/p/18681173