Add files via upload

Author: ShuZishan

R.mat

@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul 30 10:02:04 2022
+
+@author: pku
+"""
+
+import os
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras import applications
+from tensorflow.keras import optimizers
+from tensorflow.keras.models import Sequential, Model
+from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping
+from tensorflow.keras.layers import Flatten, Dense, Dropout, Input
+from tensorflow.keras.applications.resnet import preprocess_input
+import matplotlib.pyplot as plt 
+import h5py
+#%%
+img_dir = './Pics/'
+val_img_dir = './valPics/'
+nb_train_samples = 16000
+nb_val_samples = 500
+im_size = 165
+
+imgs = []
+
+for i in range(nb_train_samples):
+    img = Image.open(os.path.join(img_dir,'%d' % (i+1) + '.bmp'))
+    img = img.resize((im_size,im_size),Image.BICUBIC)
+    img1 = np.array(img)
+    imgs.append(img1)
+
+imgs = np.stack(imgs)
+
+
+val_imgs = []
+
+for i in range(nb_val_samples):
+    img = Image.open(os.path.join(val_img_dir,'%d' % (i+1) + '.bmp'))
+    img = img.resize((im_size,im_size),Image.BICUBIC)
+    img1 = np.array(img)
+    val_imgs.append(img1)
+
+val_imgs = np.stack(val_imgs)
+#%%
+train_x = preprocess_input(imgs)
+val_x = preprocess_input(val_imgs)
+#%%
+
+config = tf.compat.v1.ConfigProto()
+config.gpu_options.allow_growth = True
+session = tf.compat.v1.InteractiveSession(config=config)
+
+
+input_tensor = Input(shape=(im_size, im_size, 3))
+base = applications.ResNet50(input_tensor=input_tensor, weights='imagenet', include_top= False)
+base.summary()
+#%%
+x = base.layers[132].output
+model = Model(inputs = base.input, outputs = x)
+model.summary()
+wi = model.outputs[0].shape[1]
+fi = model.outputs[0].shape[3]
+#%%
+# train_fea = np.zeros((nb_train_samples,wi,wi,fi),'float32')
+
+# loop = int(nb_train_samples/1000)
+# for i in range(loop):
+#     train_fea[(i*1000):((i+1)*1000)] = model.predict(train_x[(i*1000):((i+1)*1000)])
+
+# val_fea = model.predict(val_x)
+
+# np.save('train_r132.npy',train_fea)
+# np.save('val_r132.npy',val_fea)

R_0.npy

@@ -0,0 +1,69 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul 30 10:02:04 2022
+
+@author: pku
+"""
+
+import os
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras import applications
+from tensorflow.keras import optimizers
+from tensorflow.keras.models import Sequential, Model
+from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping
+from tensorflow.keras.layers import Flatten, Dense, Dropout, Input
+from tensorflow.keras.applications.xception import preprocess_input
+import matplotlib.pyplot as plt 
+import h5py
+#%%
+img_dir = './Pics/'
+val_img_dir = './valPics/'
+nb_train_samples = 16000
+nb_val_samples = 500
+im_size = 165
+
+imgs = []
+
+for i in range(nb_train_samples):
+    img = Image.open(os.path.join(img_dir,'%d' % (i+1) + '.bmp'))
+    img = img.resize((im_size,im_size),Image.BICUBIC)
+    img1 = np.array(img)
+    imgs.append(img1)
+
+imgs = np.stack(imgs)
+
+
+val_imgs = []
+
+for i in range(nb_val_samples):
+    img = Image.open(os.path.join(val_img_dir,'%d' % (i+1) + '.bmp'))
+    img = img.resize((im_size,im_size),Image.BICUBIC)
+    img1 = np.array(img)
+    val_imgs.append(img1)
+
+val_imgs = np.stack(val_imgs)
+#%%
+train_x = preprocess_input(imgs)
+val_x = preprocess_input(val_imgs)
+#%%
+
+config = tf.compat.v1.ConfigProto()
+config.gpu_options.allow_growth = True
+session = tf.compat.v1.InteractiveSession(config=config)
+
+
+input_tensor = Input(shape=(im_size, im_size, 3))
+base = applications.Xception(input_tensor=input_tensor, weights='imagenet', include_top=True)
+base.summary()
+#%%
+x = base.layers[77].output
+model = Model(inputs = base.input, outputs = x)
+model.summary()
+#%%
+train_fea = model.predict(train_x)
+val_fea = model.predict(val_x)
+
+np.save('train_x77.npy',train_fea)
+np.save('val_x77.npy',val_fea)

R_1.npy

@@ -0,0 +1,146 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jan 14 11:32:37 2020
+
+@author: pku
+"""
+#%%
+import os
+import numpy as np
+from PIL import Image
+from tensorflow.keras import applications
+from tensorflow.keras import optimizers
+from tensorflow.keras.models import Sequential, Model
+from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping
+from tensorflow.keras.layers import Flatten, Dense, Dropout
+
+import matplotlib.pyplot as plt 
+import h5py
+#%%
+
+
+nb_train_samples = 16000
+nb_validation_samples = 500
+
+
+#%%
+
+data = np.load('../Rsp.npy')
+test = np.load('../valRsp.npy')
+
+
+n = nb_train_samples
+train_x1 = np.load('../train_r132.npy')
+val_x1 = np.load('../val_r132.npy')
+
+#%%
+
+n1 = 11*11*1024
+STD1 = np.std(train_x1)
+
+
+train_x = np.reshape(train_x1,(n,n1))/STD1
+val_x = np.reshape(val_x1,(nb_validation_samples,n1))/STD1
+
+
+train_y = np.reshape(data,(n,128*128))
+val_y   = np.reshape(test,(nb_validation_samples,128*128))
+val_y1 = np.reshape(val_y,(nb_validation_samples,128,128,1))
+
+
+fnum = 128*128
+ROI = np.mean(test**2,0)>0
+roi = np.reshape(ROI,(fnum))
+Cnum = 200
+
+L1 = 1e-4
+#%%
+from tensorflow.keras.layers import Conv2D,BatchNormalization, MaxPooling2D, DepthwiseConv2D, Activation, GaussianNoise, LocallyConnected1D, Reshape, Conv2DTranspose
+from tensorflow.keras import regularizers
+from tensorflow.keras import activations
+from tensorflow.keras.models import load_model
+from tensorflow.keras import backend as K
+import tensorflow as tf
+
+
+
+def mean_squared_error_noise(y_true, y_pred):
+    return K.mean(K.square(K.relu(K.abs(y_pred - y_true)-0.0)), axis=-1)
+
+# to deal with Failed to get convolution algorithm. 
+config = tf.compat.v1.ConfigProto()
+config.gpu_options.allow_growth = True
+session = tf.compat.v1.InteractiveSession(config=config)
+
+#alexnet = load_model('alexnet.h5')
+model = Sequential()
+model.add(Dropout(0.1,input_shape=(n1,)))
+model.add(Dense(Cnum, kernel_regularizer=regularizers.l1(L1/Cnum), activation='elu'))
+model.add(Dense(fnum, use_bias=True))
+
+
+w1 = model.layers[-1].get_weights()
+w2 = np.zeros((Cnum, fnum))
+w3 = np.zeros((fnum))
+w3[roi] = 0.001
+for i in range(Cnum):
+    w2[i,:] = w3
+
+w1[0] = w2
+model.layers[-1].set_weights(w1)
+
+
+#adadelta=optimizers.Adadelta(lr=0.001, rho=0.95, epsilon=1e-06)
+Adam = optimizers.Adam(lr=0.00002, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
+#model.layers[1].kernel_regularizer=regularizers.l1(1e-2/Cnum)
+
+model.compile(loss=mean_squared_error_noise, optimizer=Adam, metrics=['mse'])
+model.summary()
+model.save_weights('initial.hdf5')
+
+    
+
+#%%
+
+filepath="Cell.hdf5"
+model.load_weights('initial.hdf5')
+
+#earlyStopping=EarlyStopping(monitor='val_mse', patience=30, verbose=1, mode='auto')
+saveBestModel = ModelCheckpoint(filepath, monitor='val_mse', verbose=1, save_best_only=True, mode='auto')
+#callbacks_list = [earlyStopping,saveBestModel] 
+callbacks_list = [saveBestModel] 
+
+
+model.fit(train_x, train_y, epochs=20, batch_size=20, validation_data=(val_x, val_y), callbacks=callbacks_list)
+
+#%% test trained model
+model.load_weights(filepath)
+pred = model.predict(val_x)
+pred1 = np.reshape(pred,(nb_validation_samples,128,128,1))
+
+R = np.zeros((128,128))
+VE = np.zeros((128,128))
+for i in range(128):
+    for j in range(128):
+        if np.sum(np.abs(val_y1[:,i,j,0]))>0:
+            
+            u2=np.zeros((2,nb_validation_samples))
+            u2[0,:]=np.reshape(pred1[:,i,j,0],(nb_validation_samples))
+            u2[1,:]=np.reshape(val_y1[:,i,j,0],(nb_validation_samples))
+        
+        
+                
+            c2=np.corrcoef(u2)
+            R[i,j] = c2[0,1]
+            VE[i,j] = 1-np.var(pred1[:,i,j,0]-val_y1[:,i,j,0])/np.var(val_y1[:,i,j,0])
+
+
+np.save('R.npy',R)
+np.save('VE.npy',VE)
+
+print('L1:',L1)
+print('R:',np.mean(R[ROI]))
+    
+K.clear_session() 
+
+