import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
np.random.seed(4213)
data = np.random.randint(low=1,high=29, size=(500, 160, 160, 10))
labels = np.random.randint(low=0,high=5, size=(500, 160, 160))
nclass = len(np.unique(labels))
print (nclass)
samples, width, height, nbands = data.shape
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.25, random_state=421)
print (X_train.shape)
print (y_train.shape)
arch = tf.keras.applications.VGG16(input_shape=[width, height, nbands],
include_top=False,
weights=None)
model = tf.keras.Sequential()
model.add(arch)
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(nclass))
model.compile(optimizer = tf.keras.optimizers.Adam(0.0001),
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
model.fit(X_train,
y_train,
epochs=3,
batch_size=32,
verbose=2)
res = model.predict(X_test)
print(res.shape)
当semantic segmentation
我为我运行以上代码时,发生了异常:
InvalidArgumentError
Incompatible shapes: [32,160,160] vs. [32]
[[node Equal (defined at c...:38) ]] [Op:__inference_train_function_1815]
tensorflow.python.framework.errors_impl.InvalidArgumentError
你的问题来自于最后一层的大小(以避免这些错误总是希望使用Python常数N_IMAGES
,WIDTH
,HEIGHT
,N_CHANNELS
和N_CLASSES
):
您应该为每个图像分配一个标签。尝试切换labels
:
import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
np.random.seed(4213)
N_IMAGES, WIDTH, HEIGHT, N_CHANNELS = (500, 160, 160, 10)
N_CLASSES = 5
data = np.random.randint(low=1,high=29, size=(N_IMAGES, WIDTH, HEIGHT, N_CHANNELS))
labels = np.random.randint(low=0,high=N_CLASSES, size=(N_IMAGES))
#...
确保您的分类器(网络的最后一层)的大小相应。在这种情况下,每个像素需要1个类:
#...
model = tf.keras.Sequential()
model.add(arch)
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(width * height))
model.add(tf.keras.layers.Reshape([width , height]))
#...
这是最简单的方法。相反,您可以设置多个反卷积层以用作分类器,甚至可以将arch
体系结构翻转并使用它来生成分类结果。正交地,您可以one_hot
对标签执行编码,从而将其扩展为N_CLASSES
,从而有效地乘以最后一层中的神经元数量。
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