keras +ConvLSTM2D

最新推荐文章于 2024-04-03 09:00:48 发布

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最新推荐文章于 2024-04-03 09:00:48 发布

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文章标签：人工智能 python

原文链接：http://www.cnblogs.com/luoganttcc/p/10525248.html

本文介绍了一种利用Keras库中的ConvLSTM2D和Convolution2D层进行图像超分辨率的技术。通过创建一个深度学习模型，将低分辨率图像上采样到高分辨率，展示了如何使用Python和Keras实现这一过程。实验中，首先生成了低分辨率和高分辨率的数据集，然后构建了一个包含卷积长短期记忆网络(ConvLSTM2D)和传统卷积层(Convolution2D)的模型进行训练，最终评估了模型的预测效果。

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import numpy as np, scipy.ndimage, matplotlib.pyplot as plt
from scipy import stats
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, ConvLSTM2D, MaxPooling2D, UpSampling2D
from sklearn.metrics import accuracy_score, confusion_matrix, cohen_kappa_score
from sklearn.preprocessing import MinMaxScaler, StandardScaler
np.random.seed(123)

raw = np.arange(96).reshape(8,3,4)
data1 = scipy.ndimage.zoom(raw, zoom=(1,100,100), order=1, mode='nearest') #low res
print (data1.shape)
#(8, 300, 400)

data2 = scipy.ndimage.zoom(raw, zoom=(1,100,100), order=3, mode='nearest') #high res
print (data2.shape)
#(8, 300, 400)

X_train = data1.reshape(1, data1.shape[0], data1.shape[1], data1.shape[2], 1)
Y_train = data2.reshape(1, data2.shape[0], data2.shape[1], data2.shape[2], 1)

model = Sequential()
input_shape = (data1.shape[0], data1.shape[1], data1.shape[2], 1)
model.add(ConvLSTM2D(16, kernel_size=(3, 3), activation='sigmoid', padding='same',input_shape=input_shape,return_sequences=True))
model.add(ConvLSTM2D(1, kernel_size=(3, 3), activation='sigmoid', padding='same',return_sequences=True))
model.compile(loss='mse', optimizer='adam')

model.fit(X_train, Y_train, 
      batch_size=1, epochs=10, verbose=1)

y_predict = model.predict(X_train)
y_predict = y_predict.reshape(data1.shape[0], data1.shape[1], data1.shape[2])
slope, intercept, r_value, p_value, std_err = stats.linregress(data2[0,:,:].reshape(-1), y_predict[0,:,:].reshape(-1))
print (r_value**2)

X_train = data1.reshape(data1.shape[0], data1.shape[1], data1.shape[2], 1)
Y_train = data2.reshape(data2.shape[0], data2.shape[1], data2.shape[2], 1)

model = Sequential()
input_shape = (data1.shape[1], data1.shape[2], 1)
model.add(Convolution2D(64, kernel_size=(3,3), activation='sigmoid',padding='same',input_shape=input_shape))        
model.add(Convolution2D(1, kernel_size=(3,3), activation='sigmoid',padding='same'))

model.compile(loss='mse', optimizer='adam')

model.fit(X_train, Y_train, 
          batch_size=1, epochs=10, verbose=1)
y_predict = model.predict(X_train)
y_predict = y_predict.reshape(data1.shape[0], data1.shape[1], data1.shape[2])

slope, intercept, r_value, p_value, std_err = stats.linregress(data2[0,:,:].reshape(-1), y_predict[0,:,:].reshape(-1))
print (r_value**2)

转载于:https://www.cnblogs.com/luoganttcc/p/10525248.html