层 "model" 的输入 0 与层不兼容： expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)
Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)
我正在为二进制 class 训练一个 Unet 分割模型。数据集加载到 tensorflow 数据管道中。图像是 (512, 512, 3) 形状，面具是 (512, 512, 1) 形状。该模型期望 (512, 512, 3) 形状的输入。但是我收到以下错误。
“模型”层的输入 0 与该层不兼容：预期形状=(None, 512, 512, 3)，找到的形状=(512, 512, 3)
这是元数据数据框中的图像。
将索引随机抽样到 select 训练和验证集
num_samples = train_metadata.shape[0]
train_indices = np.random.choice(range(num_samples), int(num_samples * 0.8), replace=False)
valid_indices = list(set(range(num_samples)) - set(train_indices))
train_samples = train_metadata.iloc[train_indices, ]
valid_samples = train_metadata.iloc[valid_indices, ]
维度
IMG_WIDTH = 512
IMG_HEIGHT = 512
IMG_CHANNELS = 3
训练图像解析函数
def parse_function_train_images(image_path):
image_path = image_path
mask_path = tf.strings.regex_replace(image_path, "sat", "mask")
mask_path = tf.strings.regex_replace(mask_path, "jpg", "png")
image = tf.io.read_file(image_path)
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.convert_image_dtype(image, tf.uint8)
image = tf.image.resize(image, (IMG_WIDTH, IMG_HEIGHT))
#image = tf.expand_dims(image, axis=0)
mask = tf.io.read_file(mask_path)
mask = tf.image.decode_png(mask, channels=1)
mask = tf.image.convert_image_dtype(mask, tf.uint8)
mask = tf.image.resize(mask, (IMG_WIDTH, IMG_HEIGHT))
#mask = tf.where(mask == 255, np.dtype("uint8").type(0), mask)
return image, mask
测试图片解析函数
def parse_function_test_images(image_path):
image = tf.io.read_file(image_path)
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.convert_image_dtype(image, tf.uint8)
image = tf.image.resize(image, (IMG_WIDTH, IMG_HEIGHT))
#image = tf.expand_dims(image, axis=0)
return image
正在加载数据集
ds = tf.data.Dataset.from_tensor_slices(train_samples["sat_with_path"].values)
train_dataset = ds.map(parse_function_train_images)
validation_ds = tf.data.Dataset.from_tensor_slices(valid_samples["sat_with_path"].values)
validation_dataset = validation_ds.map(parse_function_train_images)
test_ds = tf.data.Dataset.from_tensor_slices(test_metadata["sat_with_path"].values)
test_dataset = test_ds.map(parse_function_test_images)
标准化图像
def normalize(image, mask):
image = tf.cast(image, tf.float32) / 255.0
mask = tf.cast(mask, tf.float32) / 255.0
return image, mask
def test_normalize(image):
image = tf.cast(image, tf.float32) / 255.0
return image
TRAIN_LENGTH = len(train_dataset)
BATCH_SIZE = 64
BUFFER_SIZE = 1000
STEPS_PER_EPOCH = TRAIN_LENGTH // BATCH_SIZE
映射数据集
train_images = train_dataset.map(normalize, num_parallel_calls=tf.data.AUTOTUNE)
validation_images = validation_dataset.map(normalize, num_parallel_calls=tf.data.AUTOTUNE)
test_images = test_dataset.map(test_normalize, num_parallel_calls=tf.data.AUTOTUNE)
增强层
class Augment(tf.keras.layers.Layer):
def __init__(self, seed=42):
super().__init__()
self.augment_inputs = tf.keras.layers.RandomFlip(mode="horizontal", seed=seed)
self.augment_labels = tf.keras.layers.RandomFlip(mode="horizontal", seed=seed)
def call(self, inputs, labels):
inputs = self.augment_inputs(inputs)
inputs = tf.expand_dims(inputs, axis=0)
labels = self.augment_labels(labels)
return inputs, labels
train_batches = (
train_images
.cache()
.shuffle(BUFFER_SIZE)
.batch(BATCH_SIZE)
.repeat()
.map(Augment())
.prefetch(buffer_size=tf.data.AUTOTUNE)
)
validation_batches = (
validation_images
.cache()
.shuffle(BUFFER_SIZE)
.batch(BATCH_SIZE)
.repeat()
.map(Augment())
.prefetch(buffer_size=tf.data.AUTOTUNE)
)
test_batches = test_images.batch(BATCH_SIZE)
Unet 模型
inputs = tf.keras.layers.Input((IMG_WIDTH, IMG_HEIGHT, IMG_CHANNELS))
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer="he_normal", padding="same")(inputs)
c1 = tf.keras.layers.Dropout(0.1)(c1)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer="he_normal", padding="same")(c1)
p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p1)
c2 = tf.keras.layers.Dropout(0.1)(c2)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c2)
p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p2)
c3 = tf.keras.layers.Dropout(0.2)(c3)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c3)
p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p3)
c4 = tf.keras.layers.Dropout(0.2)(c4)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c4)
p4 = tf.keras.layers.MaxPooling2D((2, 2))(c4)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p4)
c5 = tf.keras.layers.Dropout(0.3)(c5)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c5)
u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding="same")(c5)
u6 = tf.keras.layers.concatenate([u6, c4])
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u6)
c6 = tf.keras.layers.Dropout(0.2)(c6)
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c6)
u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding="same")(c6)
u7 = tf.keras.layers.concatenate([u7, c3])
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u7)
c7 = tf.keras.layers.Dropout(0.2)(c7)
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c7)
u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding="same")(c7)
u8 = tf.keras.layers.concatenate([u8, c2])
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u8)
c8 = tf.keras.layers.Dropout(0.1)(c8)
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c8)
u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding="same")(c8)
u9 = tf.keras.layers.concatenate([u9, c1], axis=3)
c9 = tf.keras.layers.Conv2D(16, (3, 3), strides=(2, 2), padding="same")(u9)
c9 = tf.keras.layers.Dropout(0.1)(c9)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c9)
outputs = tf.keras.layers.Conv2D(1, (1, 1), activation="sigmoid")(c9)
model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.summary()
checkpointer = tf.keras.callbacks.ModelCheckpoint('model_for_nuclie.h5', verbose=1, save_best_only=True)
callbacks = [
tf.keras.callbacks.EarlyStopping(patience=2, monitor="val_loss"),
tf.keras.callbacks.TensorBoard(log_dir="logs"),
checkpointer
]
使模型适合数据
results = model.fit(train_images, validation_data=validation_images, \
batch_size=16, epochs=25, callbacks=callbacks
)
错误：
在 model.fit 中使用 train_batches 而不是 train_images。此外，您不需要使用 repeat()，如果您不指定要重复数据集的次数，它会导致无限数据集。关于您的标签错误，请尝试像这样重写您的模型：
import tensorflow as tf
inputs = tf.keras.layers.Input((512, 512, 3))
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer="he_normal", padding="same")(inputs)
c1 = tf.keras.layers.Dropout(0.1)(c1)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer="he_normal", padding="same")(c1)
p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p1)
c2 = tf.keras.layers.Dropout(0.1)(c2)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c2)
p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p2)
c3 = tf.keras.layers.Dropout(0.2)(c3)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c3)
p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p3)
c4 = tf.keras.layers.Dropout(0.2)(c4)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c4)
p4 = tf.keras.layers.MaxPooling2D((2, 2))(c4)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p4)
c5 = tf.keras.layers.Dropout(0.3)(c5)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c5)
u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding="same")(c5)
u6 = tf.keras.layers.concatenate([u6, c4])
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u6)
c6 = tf.keras.layers.Dropout(0.2)(c6)
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c6)
u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding="same")(c6)
u7 = tf.keras.layers.concatenate([u7, c3])
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u7)
c7 = tf.keras.layers.Dropout(0.2)(c7)
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c7)
u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding="same")(c7)
u8 = tf.keras.layers.concatenate([u8, c2])
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u8)
c8 = tf.keras.layers.Dropout(0.1)(c8)
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c8)
u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding="same")(c8)
u9 = tf.keras.layers.concatenate([u9, c1], axis=3)
outputs = tf.keras.layers.Conv2D(1, (1, 1), activation="sigmoid")(u9)
model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.summary()
我正在为二进制 class 训练一个 Unet 分割模型。数据集加载到 tensorflow 数据管道中。图像是 (512, 512, 3) 形状，面具是 (512, 512, 1) 形状。该模型期望 (512, 512, 3) 形状的输入。但是我收到以下错误。 “模型”层的输入 0 与该层不兼容：预期形状=(None, 512, 512, 3)，找到的形状=(512, 512, 3)
这是元数据数据框中的图像。
将索引随机抽样到 select 训练和验证集
num_samples = train_metadata.shape[0]
train_indices = np.random.choice(range(num_samples), int(num_samples * 0.8), replace=False)
valid_indices = list(set(range(num_samples)) - set(train_indices))
train_samples = train_metadata.iloc[train_indices, ]
valid_samples = train_metadata.iloc[valid_indices, ]
维度
IMG_WIDTH = 512
IMG_HEIGHT = 512
IMG_CHANNELS = 3
训练图像解析函数
def parse_function_train_images(image_path):
image_path = image_path
mask_path = tf.strings.regex_replace(image_path, "sat", "mask")
mask_path = tf.strings.regex_replace(mask_path, "jpg", "png")
image = tf.io.read_file(image_path)
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.convert_image_dtype(image, tf.uint8)
image = tf.image.resize(image, (IMG_WIDTH, IMG_HEIGHT))
#image = tf.expand_dims(image, axis=0)
mask = tf.io.read_file(mask_path)
mask = tf.image.decode_png(mask, channels=1)
mask = tf.image.convert_image_dtype(mask, tf.uint8)
mask = tf.image.resize(mask, (IMG_WIDTH, IMG_HEIGHT))
#mask = tf.where(mask == 255, np.dtype("uint8").type(0), mask)
return image, mask
测试图片解析函数
def parse_function_test_images(image_path):
image = tf.io.read_file(image_path)
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.convert_image_dtype(image, tf.uint8)
image = tf.image.resize(image, (IMG_WIDTH, IMG_HEIGHT))
#image = tf.expand_dims(image, axis=0)
return image
正在加载数据集
ds = tf.data.Dataset.from_tensor_slices(train_samples["sat_with_path"].values)
train_dataset = ds.map(parse_function_train_images)
validation_ds = tf.data.Dataset.from_tensor_slices(valid_samples["sat_with_path"].values)
validation_dataset = validation_ds.map(parse_function_train_images)
test_ds = tf.data.Dataset.from_tensor_slices(test_metadata["sat_with_path"].values)
test_dataset = test_ds.map(parse_function_test_images)
标准化图像
def normalize(image, mask):
image = tf.cast(image, tf.float32) / 255.0
mask = tf.cast(mask, tf.float32) / 255.0
return image, mask
def test_normalize(image):
image = tf.cast(image, tf.float32) / 255.0
return image
TRAIN_LENGTH = len(train_dataset)
BATCH_SIZE = 64
BUFFER_SIZE = 1000
STEPS_PER_EPOCH = TRAIN_LENGTH // BATCH_SIZE
映射数据集
train_images = train_dataset.map(normalize, num_parallel_calls=tf.data.AUTOTUNE)
validation_images = validation_dataset.map(normalize, num_parallel_calls=tf.data.AUTOTUNE)
test_images = test_dataset.map(test_normalize, num_parallel_calls=tf.data.AUTOTUNE)
增强层
class Augment(tf.keras.layers.Layer):
def __init__(self, seed=42):
super().__init__()
self.augment_inputs = tf.keras.layers.RandomFlip(mode="horizontal", seed=seed)
self.augment_labels = tf.keras.layers.RandomFlip(mode="horizontal", seed=seed)
def call(self, inputs, labels):
inputs = self.augment_inputs(inputs)
inputs = tf.expand_dims(inputs, axis=0)
labels = self.augment_labels(labels)
return inputs, labels
train_batches = (
train_images
.cache()
.shuffle(BUFFER_SIZE)
.batch(BATCH_SIZE)
.repeat()
.map(Augment())
.prefetch(buffer_size=tf.data.AUTOTUNE)
)
validation_batches = (
validation_images
.cache()
.shuffle(BUFFER_SIZE)
.batch(BATCH_SIZE)
.repeat()
.map(Augment())
.prefetch(buffer_size=tf.data.AUTOTUNE)
)
test_batches = test_images.batch(BATCH_SIZE)
Unet 模型
inputs = tf.keras.layers.Input((IMG_WIDTH, IMG_HEIGHT, IMG_CHANNELS))
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer="he_normal", padding="same")(inputs)
c1 = tf.keras.layers.Dropout(0.1)(c1)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer="he_normal", padding="same")(c1)
p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p1)
c2 = tf.keras.layers.Dropout(0.1)(c2)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c2)
p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p2)
c3 = tf.keras.layers.Dropout(0.2)(c3)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c3)
p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p3)
c4 = tf.keras.layers.Dropout(0.2)(c4)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c4)
p4 = tf.keras.layers.MaxPooling2D((2, 2))(c4)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p4)
c5 = tf.keras.layers.Dropout(0.3)(c5)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c5)
u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding="same")(c5)
u6 = tf.keras.layers.concatenate([u6, c4])
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u6)
c6 = tf.keras.layers.Dropout(0.2)(c6)
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c6)
u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding="same")(c6)
u7 = tf.keras.layers.concatenate([u7, c3])
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u7)
c7 = tf.keras.layers.Dropout(0.2)(c7)
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c7)
u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding="same")(c7)
u8 = tf.keras.layers.concatenate([u8, c2])
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u8)
c8 = tf.keras.layers.Dropout(0.1)(c8)
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c8)
u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding="same")(c8)
u9 = tf.keras.layers.concatenate([u9, c1], axis=3)
c9 = tf.keras.layers.Conv2D(16, (3, 3), strides=(2, 2), padding="same")(u9)
c9 = tf.keras.layers.Dropout(0.1)(c9)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c9)
outputs = tf.keras.layers.Conv2D(1, (1, 1), activation="sigmoid")(c9)
model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.summary()
checkpointer = tf.keras.callbacks.ModelCheckpoint('model_for_nuclie.h5', verbose=1, save_best_only=True)
callbacks = [
tf.keras.callbacks.EarlyStopping(patience=2, monitor="val_loss"),
tf.keras.callbacks.TensorBoard(log_dir="logs"),
checkpointer
]
使模型适合数据
results = model.fit(train_images, validation_data=validation_images, \
batch_size=16, epochs=25, callbacks=callbacks
)
错误：
在 model.fit 中使用 train_batches 而不是 train_images。此外，您不需要使用 repeat()，如果您不指定要重复数据集的次数，它会导致无限数据集。关于您的标签错误，请尝试像这样重写您的模型：
import tensorflow as tf
inputs = tf.keras.layers.Input((512, 512, 3))
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer="he_normal", padding="same")(inputs)
c1 = tf.keras.layers.Dropout(0.1)(c1)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer="he_normal", padding="same")(c1)
p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p1)
c2 = tf.keras.layers.Dropout(0.1)(c2)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c2)
p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p2)
c3 = tf.keras.layers.Dropout(0.2)(c3)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c3)
p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p3)
c4 = tf.keras.layers.Dropout(0.2)(c4)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c4)
p4 = tf.keras.layers.MaxPooling2D((2, 2))(c4)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(p4)
c5 = tf.keras.layers.Dropout(0.3)(c5)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c5)
u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding="same")(c5)
u6 = tf.keras.layers.concatenate([u6, c4])
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u6)
c6 = tf.keras.layers.Dropout(0.2)(c6)
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c6)
u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding="same")(c6)
u7 = tf.keras.layers.concatenate([u7, c3])
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u7)
c7 = tf.keras.layers.Dropout(0.2)(c7)
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c7)
u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding="same")(c7)
u8 = tf.keras.layers.concatenate([u8, c2])
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(u8)
c8 = tf.keras.layers.Dropout(0.1)(c8)
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same")(c8)
u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding="same")(c8)
u9 = tf.keras.layers.concatenate([u9, c1], axis=3)
outputs = tf.keras.layers.Conv2D(1, (1, 1), activation="sigmoid")(u9)
model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.summary()