import matplotlib.pyplot as pltimport numpy as npimport PILimport tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras import layersfrom tensorflow.keras.models import Sequential
读取图片
import pathlibdata_dir = r'C:\Users\86188\TensorFlow机器学习\CNN图像分类\五类图像'data_dir = pathlib.Path(data_dir)print(data_dir)
image_count = len(list(data_dir.glob('*/*.jpeg')))print(image_count)
查看图片：
腹部CT = list(data_dir.glob('腹部CT/*'))PIL.Image.open(str(腹部CT[0]))
脑部CT = list(data_dir.glob('脑部CT/*'))PIL.Image.open(str(脑部CT[0]))
手X光片 = list(data_dir.glob('手X光片/*'))PIL.Image.open(str(手X光片[0]))
胸部CT = list(data_dir.glob('胸部CT/*'))PIL.Image.open(str(胸部CT[0]))
胸部X光片 = list(data_dir.glob('胸部X光片/*'))PIL.Image.open(str(胸部X光片[0]))
定义一些参数：
#为加载器定义一些参数batch_size = 32img_height = 90img_width = 90
使用 80% 的图像进行训练，使用 20% 的图像进行验证：
train_ds = tf.keras.utils.image_dataset_from_directory(data_dir,validation_split=0.2,subset="training",seed=123,image_size=(img_height, img_width),batch_size=batch_size)
val_ds = tf.keras.utils.image_dataset_from_directory(data_dir,validation_split=0.2,subset="validation",seed=123,image_size=(img_height, img_width),batch_size=batch_size)
class_names = train_ds.class_namesprint(class_names)
import matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei']plt.figure(figsize=(10, 10))for images, labels in train_ds.take(1):for i in range(9):ax = plt.subplot(5, 5,i+1)plt.imshow(images[i].numpy().astype("uint8"))plt.title(class_names[labels[i]])plt.axis("off")
for image_batch, labels_batch in train_ds:print(image_batch.shape)print(labels_batch.shape)break
image_batch是形状的张量(32, 90, 90, 3)。这是一批 32 张形状的图像90x90x3（最后一个维度是指颜色通道 RGB）。label_batch是 shape 的张量，(32,)这些是 32 幅图像的对应标签。
AUTOTUNE = tf.data.AUTOTUNEtrain_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
normalization_layer = layers.Rescaling(1./255)normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))image_batch, labels_batch = next(iter(normalized_ds))first_image = image_batch[0]print(np.min(first_image), np.max(first_image))
划分数据集并构建模型（CNN模型搭建）：
num_classes = len(class_names)model = Sequential([layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),layers.Conv2D(16, 3, padding='same', activation='relu'),layers.MaxPooling2D(), #最大池化层layers.Conv2D(32, 3, padding='same', activation='relu'),layers.MaxPooling2D(),layers.Conv2D(64, 3, padding='same', activation='relu'),layers.MaxPooling2D(),layers.Flatten(),layers.Dense(128, activation='relu'),layers.Dense(num_classes)])
调用compile()方法指定损失函数和要使用的优化器（选择 Adam）：
pile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])model.summary()
模型训练：
epochs=10 #迭代次数history = model.fit(train_ds,validation_data=val_ds,epochs=epochs)
从训练集和验证集可以看出，该模型在验证集和测试的准确率高达100%，不存在过拟合的情况，也不需要再使用数据增强或dropout等方法了。
绘制准确率和损失值曲线：
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=(8, 8))plt.subplot(1, 2, 1)plt.plot(epochs_range, acc, label='Training Accuracy')plt.plot(epochs_range, val_acc, label='Validation Accuracy')plt.legend(loc='lower right')plt.title('训练集和验证集的准确度')plt.subplot(1, 2, 2)plt.plot(epochs_range, loss, label='Training Loss')plt.plot(epochs_range, val_loss, label='Validation Loss')plt.legend(loc='upper right')plt.title('训练集和验证集的损失值')plt.show()
评估模型：
loss,accuracy = model.evaluate(train_ds, verbose=1) #verbose = 1 为输出进度条记录print('损失值:',loss)print('准确度:', accuracy )
进行预测：
手X光片_path = r"C:/Users/86188/TensorFlow机器学习/datasets/5ClassMedicalImg/手X光片/001017.jpeg"img = tf.keras.utils.load_img(手X光片_path, target_size=(img_height, img_width))
img_array = tf.keras.utils.img_to_array(img)img_array = tf.expand_dims(img_array, 0) # Create a batchpredictions = model.predict(img_array)score = tf.nn.softmax(predictions[0])print("这个图片大概属于 {} 有着 {:.2f}%准确度.".format(class_names[np.argmax(score)], 100 * np.max(score)))
