#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2022/8/29 21:46
# @Author : 陈伟峰
# @Site :
# @File : alexnet.py
# @Software: PyCharm
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
def getModel():
model = tf.keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=96,kernel_size=11,strides=4,input_shape=(32,32,3),padding='same',activation='relu'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPooling2D(pool_size=3,strides=2,padding='same'))
model.add(keras.layers.Conv2D(filters=256,kernel_size=5,strides=1,padding='same',activation='relu'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPooling2D(pool_size=3,strides=2,padding='same'))
model.add(keras.layers.Conv2D(filters=384,kernel_size=3,strides=1,padding='same',activation='relu'))
model.add(keras.layers.Conv2D(filters=384,kernel_size=3,strides=1,padding='same',activation='relu'))
model.add(keras.layers.Conv2D(filters=256,kernel_size=3,strides=1,padding='same',activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=3,strides=2,padding='same'))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(4096,activation='relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(4096,activation='relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(10,activation='softmax')) #修正的输出
return model
def train_history(model_train,train,val):
plt.plot(model_train.history[train])
plt.plot(model_train.history[val])
plt.title('Train History')
plt.xlabel('epoch')
plt.ylabel(train)
plt.legend(['train','validation'],loc='upper left')
if __name__ == '__main__':
(x_train_image,y_train_label),(x_test_image,y_test_label) = tf.keras.datasets.cifar10.load_data()
x_train_normalize = x_train_image.astype('float32')/255
x_test_normalize = x_test_image.astype('float32')/255
y_train_OneHot = tf.keras.utils.to_categorical(y_train_label)
y_test_OneHot = tf.keras.utils.to_categorical(y_test_label)
model = getModel()
print(model.summary())
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
model_train = model.fit(x=x_train_normalize,y=y_train_OneHot,validation_split=0.2,epochs=10,batch_size=300,verbose=1)
train_history(model_train,'loss','val_loss')
scores = model.evaluate(x_test_normalize,y_test_OneHot,verbose=2)
print(scores)
2022-08-29 22:10:14.020650: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll
2022-08-29 22:10:16.297928: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library nvcuda.dll
2022-08-29 22:10:16.312140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 3060 Laptop GPU computeCapability: 8.6
coreClock: 1.425GHz coreCount: 30 deviceMemorySize: 6.00GiB deviceMemoryBandwidth: 312.97GiB/s
2022-08-29 22:10:16.312331: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll
2022-08-29 22:10:16.332270: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublas64_11.dll
2022-08-29 22:10:16.332391: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublasLt64_11.dll
2022-08-29 22:10:16.336976: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cufft64_10.dll
2022-08-29 22:10:16.338270: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library curand64_10.dll
2022-08-29 22:10:16.347628: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusolver64_11.dll
2022-08-29 22:10:16.351422: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusparse64_11.dll
2022-08-29 22:10:16.352058: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudnn64_8.dll
2022-08-29 22:10:16.352200: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2022-08-29 22:10:16.352538: 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.
2022-08-29 22:10:16.353755: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 3060 Laptop GPU computeCapability: 8.6
coreClock: 1.425GHz coreCount: 30 deviceMemorySize: 6.00GiB deviceMemoryBandwidth: 312.97GiB/s
2022-08-29 22:10:16.354006: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2022-08-29 22:10:16.744536: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-08-29 22:10:16.744644: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0
2022-08-29 22:10:16.744707: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N
2022-08-29 22:10:16.744893: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3491 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6)
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 8, 8, 96) 34944
_________________________________________________________________
batch_normalization (BatchNo (None, 8, 8, 96) 384
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 4, 4, 96) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 4, 4, 256) 614656
_________________________________________________________________
batch_normalization_1 (Batch (None, 4, 4, 256) 1024
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 2, 2, 256) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 2, 2, 384) 885120
_________________________________________________________________
conv2d_3 (Conv2D) (None, 2, 2, 384) 1327488
_________________________________________________________________
conv2d_4 (Conv2D) (None, 2, 2, 256) 884992
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 1, 1, 256) 0
_________________________________________________________________
flatten (Flatten) (None, 256) 0
_________________________________________________________________
dense (Dense) (None, 4096) 1052672
_________________________________________________________________
dropout (Dropout) (None, 4096) 0
_________________________________________________________________
dense_1 (Dense) (None, 4096) 16781312
_________________________________________________________________
dropout_1 (Dropout) (None, 4096) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 40970
=================================================================
Total params: 21,623,562
Trainable params: 21,622,858
Non-trainable params: 704
_________________________________________________________________
None
2022-08-29 22:10:17.230099: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/10
2022-08-29 22:10:17.685067: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudnn64_8.dll
2022-08-29 22:10:18.260050: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8101
2022-08-29 22:10:19.011708: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublas64_11.dll
2022-08-29 22:10:19.574362: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublasLt64_11.dll
2022-08-29 22:10:20.055501: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
134/134 [==============================] - 7s 27ms/step - loss: 1.8268 - accuracy: 0.3137 - val_loss: 2.6363 - val_accuracy: 0.1167
Epoch 2/10
134/134 [==============================] - 3s 22ms/step - loss: 1.4673 - accuracy: 0.4662 - val_loss: 1.8395 - val_accuracy: 0.3370
Epoch 3/10
134/134 [==============================] - 3s 22ms/step - loss: 1.3143 - accuracy: 0.5273 - val_loss: 1.4623 - val_accuracy: 0.4691
Epoch 4/10
134/134 [==============================] - 3s 23ms/step - loss: 1.2102 - accuracy: 0.5704 - val_loss: 1.5012 - val_accuracy: 0.4521
Epoch 5/10
134/134 [==============================] - 3s 23ms/step - loss: 1.1259 - accuracy: 0.6013 - val_loss: 1.3672 - val_accuracy: 0.5246
Epoch 6/10
134/134 [==============================] - 3s 23ms/step - loss: 1.0284 - accuracy: 0.6403 - val_loss: 1.2904 - val_accuracy: 0.5595
Epoch 7/10
134/134 [==============================] - 3s 22ms/step - loss: 0.9591 - accuracy: 0.6643 - val_loss: 1.7211 - val_accuracy: 0.4809
Epoch 8/10
134/134 [==============================] - 3s 22ms/step - loss: 0.8855 - accuracy: 0.6919 - val_loss: 1.1592 - val_accuracy: 0.6108
Epoch 9/10
134/134 [==============================] - 3s 22ms/step - loss: 0.7972 - accuracy: 0.7238 - val_loss: 1.1367 - val_accuracy: 0.6180
Epoch 10/10
134/134 [==============================] - 3s 22ms/step - loss: 0.7214 - accuracy: 0.7510 - val_loss: 1.2211 - val_accuracy: 0.6156
313/313 - 1s - loss: 1.2561 - accuracy: 0.6045
[1.2561286687850952, 0.6044999957084656]