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具体实现
#xff08;一#xff09;环境
语言环境#xff1a;Python 3.10 编 译 器: PyCharm 框 架:
#xff08;二#xff09;具体步骤
1. 使… 本文为365天深度学习训练营 中的学习记录博客 原作者K同学啊 目标
具体实现
一环境
语言环境Python 3.10 编 译 器: PyCharm 框 架:
二具体步骤
1. 使用GPU
--------------------------utils.py-------------------
import tensorflow as tf
import PIL
import matplotlib.pyplot as plt def GPU_ON(): # 查询tensorflow版本 print(Tensorflow Version, tf.__version__) # 设置使用GPU gpus tf.config.list_physical_devices(GPU) print(gpus) if gpus: gpu0 gpus[0] # 如果有多个GPU仅使用第0个GPU tf.config.experimental.set_memory_growth(gpu0, True) # 设置GPU显存按需使用 tf.config.set_visible_devices([gpu0], GPU)使用GPU并查看数据
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import os, PIL, pathlib
from utils import GPU_ONGPU_ON() data_dir ./datasets/coffee/
data_dir pathlib.Path(data_dir) image_count len(list(data_dir.glob(*/*.png)))
print(图片总数量为:, image_count)
------------------
图片总数量为: 12002. 加载数据
# 加载数据
batch_size 32
img_height, img_width 224, 224 train_ds tf.keras.preprocessing.image_dataset_from_directory( data_dir, validation_split0.2, subsettraining, seed123, image_size(img_height, img_width), batch_sizebatch_size,
) val_ds tf.keras.preprocessing.image_dataset_from_directory( data_dir, validation_split0.2, subsetvalidation, seed123, image_size(img_height, img_width), batch_sizebatch_size,
)
--------------------
Found 1200 files belonging to 4 classes.
Using 960 files for training.
Found 1200 files belonging to 4 classes.
Using 240 files for validation.获取标签
# 获取标签
class_names train_ds.class_names
print(class_names)
------------------
[Dark, Green, Light, Medium]可视化数据:
# 可视化数据
plt.figure(figsize(10, 10))
for images, labels in train_ds.take(2): for i in range(30): ax plt.subplot(5, 6, i1) plt.imshow(images[i].numpy().astype(uint8)) plt.title(class_names[labels[i]]) plt.axis(off)
plt.show()检查一下数据
# 检查一下数据
for image_batch, labels_batch in train_ds: print(image_batch.shape) print(labels_batch.shape) break
----------------------------
(32, 224, 224, 3)
(32,)**3.**配置数据集
# 配置数据集
AUTOTUNE tf.data.AUTOTUNE
train_ds train_ds.cache().shuffle(1000).prefetch(buffer_sizeAUTOTUNE)
val_ds val_ds.cache().prefetch(buffer_sizeAUTOTUNE) normalization_layer layers.experimental.preprocessing.Rescaling(1./255)
train_ds train_ds.map(lambda x, y: (normalization_layer(x), y))
val_ds val_ds.map(lambda x, y: (normalization_layer(x), y)) image_batch, labels_batch next(iter(train_ds))
first_image image_batch[0] # 查看归一化后的数据
print(np.min(first_image), np.max(first_image))
--------------------
0.0 1.04.搭建VGG-16网络
本次准备直接调用官方模型
# 搭建VGG-16网络模型
model tf.keras.applications.VGG16(weightsimagenet)
print(model.summary())
-------------------------------
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5
553467096/553467096 [] - 14s 0us/step
Model: vgg16
_________________________________________________________________Layer (type) Output Shape Param #
input_1 (InputLayer) [(None, 224, 224, 3)] 0 block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 flatten (Flatten) (None, 25088) 0 fc1 (Dense) (None, 4096) 102764544 fc2 (Dense) (None, 4096) 16781312 predictions (Dense) (None, 1000) 4097000
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________简简单单1亿的参数的模型。哈哈。
编译一下
# 编译模型
# 设置初始学习率
initial_learning_rate 1e-4
lr_schedule tf.keras.optimizers.schedules.ExponentialDecay( initial_learning_rateinitial_learning_rate, decay_steps30, decay_rate0.92, staircaseTrue
) # 设置优化器
opt tf.keras.optimizers.Adam(learning_rateinitial_learning_rate) model.compile( optimizeropt, losstf.keras.losses.SparseCategoricalCrossentropy(from_logitsFalse), metrics[accuracy]
)训练模型:
# 训练模型
epochs 20
history model.fit( train_ds, validation_dataval_ds, epochsepochs,
)训练效果不错可视化看看
acc history.history[accuracy]
val_acc history.history[val_accuracy]loss history.history[loss]
val_loss history.history[val_loss]epochs_range range(epochs)plt.figure(figsize(12, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, labelTraining Accuracy)
plt.plot(epochs_range, val_acc, labelValidation Accuracy)
plt.legend(loclower right)
plt.title(Training and Validation Accuracy)plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, labelTraining Loss)
plt.plot(epochs_range, val_loss, labelValidation Loss)
plt.legend(locupper right)
plt.title(Training and Validation Loss)
plt.show()果然超赞。 改成动态学习率的结果
opt tf.keras.optimizers.Adam(learning_ratelr_schedule)5. 手动搭建VGG-16模型 VGG-16的网络 有13个卷积层被5个max-pooling层分割和3个全连接层FC所有卷积层过滤器的大小都是3X3步长为1进行padding。5个max-pooling层分别在第2、4、7、1013卷积层后面。每次进行池化max-pooling)后特征图的长宽都缩小一半但是channel都翻倍了一直到512。最后三个全连接层大小分别是40964096 1000,我们使用的是咖啡豆识别根据数据集的类别数量修改最后的分类数量即从1000改成len(class_names)) -----------------------------
Model: model
_________________________________________________________________Layer (type) Output Shape Param #
input_1 (InputLayer) [(None, 224, 224, 3)] 0 block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 flatten (Flatten) (None, 25088) 0 fc1 (Dense) (None, 4096) 102764544 fc2 (Dense) (None, 4096) 16781312 predictions (Dense) (None, 4) 16388
Total params: 134,276,932
Trainable params: 134,276,932
Non-trainable params: 0
_________________________________________________________________
三总结
