#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2021/7/19 8:22
# @Author : wutiande
import numpy as np
import matplotlib.image as mping
import matplotlib.pyplot as plt
import tensorflow as tf
tf.compat.v1.set_random_seed(2021)
from tensorflow.keras.optimizers import RMSprop,SGD,Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(16,(3,3),activation="relu",input_shape=(180,180,3)),
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(128,(3,3),activation="relu"),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(550,activation="relu"),
tf.keras.layers.Dropout(0.1,seed=2021),
tf.keras.layers.Dense(400,activation="relu"),
tf.keras.layers.Dropout(0.3,seed=2021),
tf.keras.layers.Dense(300,activation="relu"),
tf.keras.layers.Dropout(0.4,seed=2021),
tf.keras.layers.Dense(200,activation="relu"),
tf.keras.layers.Dropout(0.2,seed=2021),
tf.keras.layers.Dense(2,activation="softmax")
])
# 模型总结
model.summary()
adam = Adam(learning_rate=0.001)
model.compile(optimizer='adam',loss="categorical_crossentropy",metrics=['acc'])
bs = 30
train_dir = r"D:\projects\tensorflow2-learn\dataset\Dataset\train/"
validation_dir = r"D:\projects\tensorflow2-learn\dataset\Dataset\test/"
train_datagen = ImageDataGenerator(rescale=1.0/255.)
test_datagen = ImageDataGenerator(rescale=1.0/255.)
#Flow_from_directory function lets the classifier directly identify the labels from the name of the directories the image lies in
train_generator = train_datagen.flow_from_directory(train_dir,batch_size=bs,class_mode='categorical',
target_size=(180,180))
validation_generator = test_datagen.flow_from_directory(validation_dir,batch_size=bs,
class_mode='categorical',
target_size=(180,180))
# 以上将输出数据和类别信息
"""
Found 119 images belonging to 2 classes.
Found 40 images belonging to 2 classes.
"""
history = model.fit(train_generator,validation_data=validation_generator,
# steps_per_epoch=150//bs,
epochs=5,
# validation_steps=50//bs,
# verbose=2,
)
tf.saved_model.save(model,"cnnTest")
C:\Users\a\AppData\Local\Programs\Python\Python38\python.exe D:/projects/tensorflow2-learn/CNN/test1.py
2021-07-19 08:44:42.519827: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2021-07-19 08:44:42.519961: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2021-07-19 08:44:44.042458: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2021-07-19 08:44:44.042583: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)
2021-07-19 08:44:44.045917: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-DA0FST6
2021-07-19 08:44:44.046077: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-DA0FST6
2021-07-19 08:44:44.046485: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 178, 178, 16) 448
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 89, 89, 16) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 87, 87, 32) 4640
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 43, 43, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 41, 41, 64) 18496
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 20, 20, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 18, 18, 128) 73856
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 9, 9, 128) 0
_________________________________________________________________
flatten (Flatten) (None, 10368) 0
_________________________________________________________________
dense (Dense) (None, 550) 5702950
_________________________________________________________________
dropout (Dropout) (None, 550) 0
_________________________________________________________________
dense_1 (Dense) (None, 400) 220400
_________________________________________________________________
dropout_1 (Dropout) (None, 400) 0
_________________________________________________________________
dense_2 (Dense) (None, 300) 120300
_________________________________________________________________
dropout_2 (Dropout) (None, 300) 0
_________________________________________________________________
dense_3 (Dense) (None, 200) 60200
_________________________________________________________________
dropout_3 (Dropout) (None, 200) 0
_________________________________________________________________
dense_4 (Dense) (None, 2) 402
=================================================================
Total params: 6,201,692
Trainable params: 6,201,692
Non-trainable params: 0
_________________________________________________________________
Found 119 images belonging to 2 classes.
Found 40 images belonging to 2 classes.
2021-07-19 08:44:44.454215: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/5
4/4 [==============================] - 2s 438ms/step - loss: 0.7406 - acc: 0.4370 - val_loss: 0.6927 - val_acc: 0.5500
Epoch 2/5
4/4 [==============================] - 2s 370ms/step - loss: 0.7001 - acc: 0.3950 - val_loss: 0.6929 - val_acc: 0.5000
Epoch 3/5
4/4 [==============================] - 2s 372ms/step - loss: 0.6934 - acc: 0.4958 - val_loss: 0.6935 - val_acc: 0.5000
Epoch 4/5
4/4 [==============================] - 2s 372ms/step - loss: 0.6920 - acc: 0.5462 - val_loss: 0.6928 - val_acc: 0.5000
Epoch 5/5
4/4 [==============================] - 2s 372ms/step - loss: 0.6967 - acc: 0.4790 - val_loss: 0.6926 - val_acc: 0.5000
2021-07-19 08:44:53.492661: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
WARNING:tensorflow:FOR KERAS USERS: The object that you are saving contains one or more Keras models or layers. If you are loading the SavedModel with `tf.keras.models.load_model`, continue reading (otherwise, you may ignore the following instructions). Please change your code to save with `tf.keras.models.save_model` or `model.save`, and confirm that the file "keras.metadata" exists in the export directory. In the future, Keras will only load the SavedModels that have this file. In other words, `tf.saved_model.save` will no longer write SavedModels that can be recovered as Keras models (this will apply in TF 2.5).
FOR DEVS: If you are overwriting _tracking_metadata in your class, this property has been used to save metadata in the SavedModel. The metadta field will be deprecated soon, so please move the metadata to a different file.
Process finished with exit code 0
数据目录
模型保存
模型的使用
见这里：这里