利用unet tensorflow深度学习
代码如下
- import os
- import numpy as np
- import matplotlib.pyplot as plt
- from skimage import io
- from keras.layers.core import Dropout
- from keras.layers.convolutional import Conv2D, Conv2DTranspose
- from keras.layers.pooling import MaxPooling2D
- from keras.layers import Input
- from keras.models import Model
- from keras.layers.merge import concatenate
- from keras.callbacks import EarlyStopping, ModelCheckpoint
-
-
- def standardize(img):
- mean = np.mean(img, axis=0)
- std = np.std(img, axis=0)
- return (img - mean) / std
-
-
- # Get training and validation paths
- def get_train_val_paths(img_folder, mask_folder, split_ratio=0.8):
- img_names = os.listdir(img_folder)
- print("total images: ", len(img_names))
- mask_names = os.listdir(mask_folder)
- img_paths = [os.path.join(img_folder, name) for name in img_names]
- mask_paths = [os.path.join(mask_folder, name) for name in mask_names]
- no_samples = len(img_paths)
- no_train = int(np.ceil(split_ratio * no_samples))
- train_paths = {name: paths_list for name, paths_list in zip(["train_imgs", "train_mask"],
- [img_paths[:no_train], mask_paths[:no_train]])}
- val_paths = {name: paths_list for name, paths_list in zip(["val_imgs", "val_mask"],
- [img_paths[no_train:], mask_paths[no_train:]])}
- return train_paths, val_paths
-
-
- # Data generator to get single image and mask
- def image_generator(img_paths, mask_paths):
-
- for img_path, mask_path in zip(img_paths, mask_paths):
- img = io.imread(img_path)
- img = standardize(img)
- img = np.expand_dims(img, axis=2)
- mask = io.imread(mask_path) / 255.
- mask = np.expand_dims(mask, axis=2)
- yield img, mask
-
-
- # Batch data generator
- def img_batch_generator(img_paths, mask_paths, batch_size):
-
- while True:
- img_gen = image_generator(img_paths, mask_paths)
-
- img_batch, mask_batch = [], []
- for img, mask in img_gen:
- img_batch.append(img)
- mask_batch.append(mask)
- if len(img_batch) == batch_size:
- yield np.stack(img_batch, axis=0), np.stack(mask_batch, axis=0)
- img_batch, mask_batch = [], []
- if len(img_batch) != 0:
- yield np.stack(img_batch, axis=0), np.stack(mask_batch, axis=0)
-
-
- image_folder = "C:/Users/11962/Desktop/images1/"
- masks_folder = "C:/Users/11962/Desktop/masks1/"
- tr_paths, v_paths = get_train_val_paths(image_folder, masks_folder)
-
- inputs = Input((512, 512, 1))
-
- c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(inputs)
- c1 = Dropout(0.1)(c1)
- c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c1)
- p1 = MaxPooling2D((2, 2))(c1)
-
- c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(p1)
- c2 = Dropout(0.1)(c2)
- c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c2)
- p2 = MaxPooling2D((2, 2))(c2)
-
- c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(p2)
- c3 = Dropout(0.2)(c3)
- c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c3)
- p3 = MaxPooling2D((2, 2))(c3)
-
- c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(p3)
- c4 = Dropout(0.2)(c4)
- c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c4)
- p4 = MaxPooling2D(pool_size=(2, 2))(c4)
-
- c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(p4)
- c5 = Dropout(0.3)(c5)
- c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c5)
-
- u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)
- u6 = concatenate([u6, c4])
- c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(u6)
- c6 = Dropout(0.2)(c6)
- c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c6)
-
- u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)
- u7 = concatenate([u7, c3])
- c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(u7)
- c7 = Dropout(0.2)(c7)
- c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c7)
-
- u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)
- u8 = concatenate([u8, c2])
- c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(u8)
- c8 = Dropout(0.1)(c8)
- c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c8)
-
- u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)
- u9 = concatenate([u9, c1], axis=3)
- c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(u9)
- c9 = Dropout(0.1)(c9)
- c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c9)
-
- outputs = Conv2D(1, (1, 1), activation='sigmoid')(c9)
-
- model = Model(inputs=[inputs], outputs=[outputs])
- model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
- print(model.summary())
- batch_size = 2
-
-
- train_gen = img_batch_generator(tr_paths["train_imgs"], tr_paths["train_mask"], batch_size)
- val_gen = img_batch_generator(v_paths["val_imgs"], v_paths["val_mask"], batch_size)
-
-
- train_steps = len(tr_paths["train_imgs"]) // batch_size
- val_steps = len(v_paths["val_imgs"]) // batch_size
-
- early_stop = EarlyStopping(patience=10, verbose=1)
- checkpoint = ModelCheckpoint("./model/keras_unet_model.h5", verbose=1, save_best_only=True)
-
- history = model.fit(train_gen, steps_per_epoch=train_steps,
- epochs=30,
- validation_data=val_gen,
- validation_steps=val_steps,
- verbose=1,
- max_queue_size=4,
- callbacks=[early_stop, checkpoint])
-
- loss = history.history["loss"]
- val_loss = history.history["val_loss"]
- acc = history.history['accuracy']
- val_acc = history.history['val_accuracy']
- epochs = range(1, len(loss) + 1)
-
- plt.plot(epochs, loss, color="red", label="training loss")
- plt.plot(epochs, val_loss, color="blue", label="validation loss")
- plt.title("Training and Validation Loss")
- plt.legend()
-
- plt.figure()
- plt.plot(epochs, acc, color="red", label="training acc")
- plt.plot(epochs, val_acc, color="blue", label="validation acc")
- plt.title("Training and Validation acc")
- plt.legend()
-
报错:TypeError: add_argument() takes 2 positional arguments but 3 were given
应该怎么修改,急