keras构建卷积神经网络

输入相关的库

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
print("TensorFlow version:", tf.__version__)

TensorFlow version: 2.6.4

导入数据

fashion_mnist = tf.keras.datasets.fashion_mnist(x_train, y_train),(x_test, y_test) = fashion_mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
x_train = x_train[..., tf.newaxis].astype("float32")
x_test = x_test[..., tf.newaxis].astype("float32")train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(128)test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(128)

2022-10-17 02:24:49.792549: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:24:49.895220: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:24:49.896028: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:24:49.902715: 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:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-10-17 02:24:49.903033: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:24:49.903894: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:24:49.904866: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:24:52.155655: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:24:52.156422: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:24:52.157115: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:24:52.157726: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15401 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0

inputs = keras.Input(shape=(28,28,1))
x = inputs
for size in [32,64,128]:x = layers.Conv2D(size, 3, strides=1, padding="same")(x)x = layers.BatchNormalization()(x)x = layers.Activation("relu")(x)x = layers.MaxPooling2D((2, 2))(x)x = layers.Conv2D(256, 3, strides=1, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(10, activation='softmax')(x)
model = keras.Model(inputs, outputs)
model.summary()

2022-10-17 02:39:04.385252: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:39:04.503544: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:39:04.504713: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:39:04.506969: 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:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-10-17 02:39:04.507300: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:39:04.508303: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:39:04.509206: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:39:07.153982: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:39:07.155140: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:39:07.156582: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-17 02:39:07.157460: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15401 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         [(None, 28, 28, 1)]       0
_________________________________________________________________
conv2d (Conv2D)              (None, 28, 28, 32)        320
_________________________________________________________________
batch_normalization (BatchNo (None, 28, 28, 32)        128
_________________________________________________________________
activation (Activation)      (None, 28, 28, 32)        0
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 14, 14, 32)        0
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 14, 14, 64)        18496
_________________________________________________________________
batch_normalization_1 (Batch (None, 14, 14, 64)        256
_________________________________________________________________
activation_1 (Activation)    (None, 14, 14, 64)        0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 7, 7, 64)          0
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 7, 7, 128)         73856
_________________________________________________________________
batch_normalization_2 (Batch (None, 7, 7, 128)         512
_________________________________________________________________
activation_2 (Activation)    (None, 7, 7, 128)         0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 3, 3, 128)         0
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 3, 3, 256)         295168
_________________________________________________________________
batch_normalization_3 (Batch (None, 3, 3, 256)         1024
_________________________________________________________________
activation_3 (Activation)    (None, 3, 3, 256)         0
_________________________________________________________________
global_average_pooling2d (Gl (None, 256)               0
_________________________________________________________________
dropout (Dropout)            (None, 256)               0
_________________________________________________________________
dense (Dense)                (None, 10)                2570
=================================================================
Total params: 392,330
Trainable params: 391,370
Non-trainable params: 960
_________________________________________________________________

model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])model.fit(train_ds, epochs=10)

Epoch 1/10

2022-10-17 02:32:49.166692: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
2022-10-17 02:32:50.227935: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005

469/469 [==============================] - 10s 6ms/step - loss: 0.4292 - accuracy: 0.8470
Epoch 2/10
469/469 [==============================] - 3s 6ms/step - loss: 0.2820 - accuracy: 0.8995
Epoch 3/10
469/469 [==============================] - 3s 6ms/step - loss: 0.2367 - accuracy: 0.9129
Epoch 4/10
469/469 [==============================] - 3s 6ms/step - loss: 0.2112 - accuracy: 0.9231
Epoch 5/10
469/469 [==============================] - 3s 7ms/step - loss: 0.1950 - accuracy: 0.9294
Epoch 6/10
469/469 [==============================] - 3s 6ms/step - loss: 0.1708 - accuracy: 0.9377
Epoch 7/10
469/469 [==============================] - 3s 6ms/step - loss: 0.1561 - accuracy: 0.9434
Epoch 8/10
469/469 [==============================] - 3s 6ms/step - loss: 0.1398 - accuracy: 0.9481
Epoch 9/10
469/469 [==============================] - 3s 6ms/step - loss: 0.1300 - accuracy: 0.9518
Epoch 10/10
469/469 [==============================] - 3s 6ms/step - loss: 0.1137 - accuracy: 0.9586

model.evaluate(test_ds)

79/79 [==============================] - 0s 3ms/step - loss: 0.2961 - accuracy: 0.9086

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