new autoencoder

Author: lukas

keras-autoencoder/denoising_autoencoder_cnn.py

@@ -0,0 +1,70 @@
+from keras.layers import Input, Dense, Flatten, Reshape, Conv2D, MaxPooling2D, UpSampling2D
+from keras.models import Model, Sequential
+from keras.callbacks import Callback
+from keras.datasets import mnist
+import numpy as np
+import wandb
+from wandb.wandb_keras import WandbKerasCallback
+
+def add_noise(x_train, x_test):
+    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.)
+    return x_train_noisy, x_test_noisy
+
+run = wandb.init()
+config = run.config
+
+config.encoding_dim = 32
+config.epochs = 10
+
+(x_train, _), (x_test, _) = mnist.load_data()
+(x_train_noisy, x_test_noisy) = add_noise(x_train, x_test)
+
+
+x_train = x_train.astype('float32') / 255.
+x_test = x_test.astype('float32') / 255.
+
+
+model = Sequential()
+#model.add(Flatten(input_shape=(28,28)))
+#model.add(Dense(config.encoding_dim, activation='relu'))
+#model.add(Dense(784, activation='sigmoid'))
+#model.add(Reshape((28,28)))
+model.add(Reshape((28,28,1), input_shape=(28,28)))
+model.add(Conv2D(32, (3,3), padding='same', activation='relu'))
+model.add(MaxPooling2D((2,2)))
+model.add(Conv2D(32, (3,3), padding='same', activation='relu'))
+model.add(UpSampling2D())
+model.add(Conv2D(1, (3,3), padding='same', activation = 'sigmoid'))
+model.add(Reshape((28,28)))
+
+
+
+model.compile(optimizer='adam', loss='mse')
+
+class Images(Callback):
+      def on_epoch_end(self, epoch, logs):
+            indices = np.random.randint(self.validation_data[0].shape[0], size=8)
+            test_data = self.validation_data[0][indices]
+            pred_data = self.model.predict(test_data)
+            run.history.row.update({
+                  "examples": [
+                        wandb.Image(np.hstack([data, pred_data[i]]), caption=str(i))
+                        for i, data in enumerate(test_data)]
+            })
+
+
+model.fit(x_train_noisy, x_train,
+                epochs=config.epochs,
+                validation_data=(x_test_noisy, x_test), callbacks=[Images(), WandbKerasCallback()])
+
+
+model.save("auto-denoise.h5")
+
+
+
+