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Author: bnsreenu

085-auto_encode_single_image_V3.0.py

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+#!/usr/bin/env python
+__author__ = "Sreenivas Bhattiprolu"
+__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"
+
+# https://www.youtube.com/watch?v=D9HjlqIrB-c
+
+
+
+
+"""
+@author: Sreenivas Bhattiprolu
+Working great.
+Good example to demo image reconstruction using autoencoders
+
+To launch tensorboard type this in the console: !tensorboard --logdir=logs/ --host localhost --port 8088
+then go to: http://localhost:8088/
+
+The ! is because we are executing shell commands from Python console.Try different optimizers and loss
+
+Try:
+Only 5 epochs, 50 epochs, 500 and 5000
+"""
+
+from matplotlib.pyplot import imshow
+import numpy as np
+import cv2
+from keras.preprocessing.image import img_to_array
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D
+from tensorflow.keras.models import Sequential
+
+
+np.random.seed(42)
+
+SIZE=256
+img_data=[]
+
+img=cv2.imread('images/monalisa.jpg', 1)   #Change 1 to 0 for grey images
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #Changing BGR to RGB to show images in true colors
+img=cv2.resize(img,(SIZE, SIZE))
+img_data.append(img_to_array(img))
+
+img_array = np.reshape(img_data, (len(img_data), SIZE, SIZE, 3))
+img_array = img_array.astype('float32') / 255.
+
+
+model = Sequential()
+model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(SIZE, SIZE, 3)))
+model.add(MaxPooling2D((2, 2), padding='same'))
+model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
+model.add(MaxPooling2D((2, 2), padding='same'))
+model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
+ 
+
+model.add(MaxPooling2D((2, 2), padding='same'))
+     
+model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
+model.add(UpSampling2D((2, 2)))
+model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
+model.add(UpSampling2D((2, 2)))
+model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
+model.add(UpSampling2D((2, 2)))
+model.add(Conv2D(3, (3, 3), activation='relu', padding='same'))
+
+model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
+model.summary()
+
+
+
+model.fit(img_array, img_array,
+        epochs=5000,
+        shuffle=True)
+
+
+print("Neural network output")
+pred = model.predict(img_array)
+
+
+
+imshow(pred[0].reshape(SIZE,SIZE,3), cmap="gray")
+

086--auto_denoise_mnist.py

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+#!/usr/bin/env python
+__author__ = "Sreenivas Bhattiprolu"
+__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"
+
+# https://www.youtube.com/watch?v=Sm54KXD-L1k
+
+
+
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D
+from tensorflow.keras.models import Sequential
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+(x_train, _), (x_test, _) = mnist.load_data()
+
+x_train = x_train.astype('float32') / 255.
+x_test = x_test.astype('float32') / 255.
+x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
+x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))
+
+#adding some noise
+noise_factor = 0.5
+x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) 
+x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) 
+
+x_train_noisy = np.clip(x_train_noisy, 0., 1.)
+x_test_noisy = np.clip(x_test_noisy, 0., 1.)
+
+#Displaying images with noise
+plt.figure(figsize=(20, 2))
+for i in range(1,10):
+    ax = plt.subplot(1, 10, i)
+    plt.imshow(x_test_noisy[i].reshape(28, 28), cmap="binary")
+plt.show()
+
+
+model = Sequential()
+model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(28, 28, 1)))
+model.add(MaxPooling2D((2, 2), padding='same'))
+model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
+model.add(MaxPooling2D((2, 2), padding='same'))
+model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
+ 
+
+model.add(MaxPooling2D((2, 2), padding='same'))
+ 
+model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
+model.add(UpSampling2D((2, 2)))
+model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
+model.add(UpSampling2D((2, 2)))
+model.add(Conv2D(32, (3, 3), activation='relu'))
+model.add(UpSampling2D((2, 2)))
+model.add(Conv2D(1, (3, 3), activation='relu', padding='same'))
+
+model.compile(optimizer='adam', loss='mean_squared_error')
+
+model.summary()
+
+model.fit(x_train_noisy, x_train, epochs=10, batch_size=256, shuffle=True, 
+          validation_data=(x_test_noisy, x_test))
+
+
+
+model.evaluate(x_test_noisy, x_test)
+
+model.save('denoising_autoencoder.model')
+
+no_noise_img = model.predict(x_test_noisy)
+
+plt.figure(figsize=(40, 4))
+for i in range(10):
+    # display original
+    ax = plt.subplot(3, 20, i + 1)
+    plt.imshow(x_test_noisy[i].reshape(28, 28), cmap="binary")
+    
+    # display reconstructed (after noise removed) image
+    ax = plt.subplot(3, 20, 40 +i+ 1)
+    plt.imshow(no_noise_img[i].reshape(28, 28), cmap="binary")
+
+plt.show()
+
+
+
+
+