Merge branch 'master' of https://github.com/lukas/ml-class

Author: l2k2

keras-autoencoder/autoencoder.py

@@ -4,21 +4,11 @@
 from keras.datasets import mnist
 from keras.datasets import fashion_mnist
 
-from keras.callbacks import Callback
 import numpy as np
+from util import Images
 import wandb
 from wandb.keras import WandbCallback
 
-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)
-        wandb.log({
-             "examples": [
-                   wandb.Image(np.hstack([data, pred_data[i]]), caption=str(i))
-                   for i, data in enumerate(test_data)]
-        }, commit=False)
 
 run = wandb.init()
 config = run.config

keras-autoencoder/autoencoder_cnn.py

@@ -2,21 +2,31 @@
 from keras.models import Model, Sequential
 from keras.datasets import mnist
 from keras.callbacks import Callback
-from autoencoder import Images
 
 import numpy as np
+from util import Images
 import wandb
 from wandb.keras import WandbCallback
+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)
+        wandb.log({
+             "examples": [
+                   wandb.Image(np.hstack([data, pred_data[i]]), caption=str(i))
+                   for i, data in enumerate(test_data)]
+        }, commit=False)
 
 run = wandb.init()
 config = run.config
 
 config.epochs = 10
 
-(x_train, _), (x_test, _) = mnist.load_data()
+(X_train, _), (X_test, _) = mnist.load_data()
 
-x_train = x_train.astype('float32') / 255.
-x_test = x_test.astype('float32') / 255.
+X_train = X_train.astype('float32') / 255.
+X_test = X_test.astype('float32') / 255.
 
 model = Sequential()
 model.add(Reshape((28,28,1), input_shape=(28,28)))
@@ -29,12 +39,10 @@
 
 model.compile(optimizer='adam', loss='mse')
 
-model.fit(x_train, x_train,
-                epochs=config.epochs,
-                validation_data=(x_test, x_test), 
-                callbacks=[Images(), WandbCallback(save_model=False)])
-
-
-model.save('auto-cnn.h5')
+model.fit(X_train, X_train,
+          epochs=config.epochs,
+          validation_data=(X_test, X_test), 
+          callbacks=[Images(), WandbCallback(save_model=False)])
 
 
+model.save('auto-cnn.h5')

keras-autoencoder/compress.ipynb

@@ -5,7 +5,7 @@
 import numpy as np
 import wandb
 from wandb.keras import WandbCallback
-from autoencoder import Images
+from util import Images
 
 def add_noise(x_train, x_test):
     noise_factor = 1.0

keras-autoencoder/denoising_autoencoder.py

@@ -5,7 +5,7 @@
 import numpy as np
 import wandb
 from wandb.keras import WandbCallback
-from autoencoder import Images
+from util import Images
 
 def add_noise(x_train, x_test):
     noise_factor = 0.5

keras-autoencoder/denoising_autoencoder_cnn.py

@@ -0,0 +1,14 @@
+from keras.callbacks import Callback
+import numpy as np
+import wandb
+
+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)
+        wandb.log({
+             "examples": [
+                   wandb.Image(np.hstack([data, pred_data[i]]), caption=str(i))
+                   for i, data in enumerate(test_data)]
+        }, commit=False)

keras-autoencoder/util.py

@@ -1,4 +1,4 @@
 [default]
 entity: qualcomm
-project: encoding-july27
+project: encoding-aug22
 base_url: https://api.wandb.ai

keras-autoencoder/wandb/settings

@@ -8,63 +8,58 @@
 
 import os
 import wandb
-from wandb.wandb_keras import WandbKerasCallback
+from wandb.keras import WandbCallback
 
 run = wandb.init()
 config = run.config
+config.dropout = 0.25
+config.dense_layer_nodes = 100
+config.learn_rate = 0.01
+config.batch_size = 32
+config.epochs = 50
+
 class_names = ['airplane','automobile','bird','cat','deer',
                'dog','frog','horse','ship','truck']
-num_classes = 10
-
+num_classes = len(class_names)
 
-save_dir = os.path.join(os.getcwd(), 'saved_models')
-model_name = 'keras_cifar10_trained_model.h5'
-
-(x_train, y_train), (x_test, y_test) = cifar10.load_data()
+(X_train, y_train), (X_test, y_test) = cifar10.load_data()
 
 # Convert class vectors to binary class matrices.
 y_train = keras.utils.to_categorical(y_train, num_classes)
 y_test = keras.utils.to_categorical(y_test, num_classes)
 
 model = Sequential()
 model.add(Conv2D(32, (3, 3), padding='same',
-                 input_shape=x_train.shape[1:], activation='relu'))
+                 input_shape=X_train.shape[1:], activation='relu'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Dropout(0.25))
+model.add(Dropout(config.dropout))
 
 model.add(Flatten())
 model.add(Dense(config.dense_layer_nodes, activation='relu'))
-model.add(Dropout(0.5))
+model.add(Dropout(config.dropout))
 model.add(Dense(num_classes, activation='softmax'))
 
-
 opt = keras.optimizers.SGD(lr=config.learn_rate)
 
 # Let's train the model using RMSprop
 model.compile(loss='categorical_crossentropy',
               optimizer=opt,
               metrics=['accuracy'])
 
-x_train = x_train.astype('float32')
-x_test = x_test.astype('float32')
-x_train /= 255
-x_test /= 255
-
-
-datagen = ImageDataGenerator(
-        width_shift_range=0.1)
-
+X_train = X_train.astype('float32') / 255.
+X_test = X_test.astype('float32') / 255.
 
-datagen.fit(x_train)
+datagen = ImageDataGenerator(width_shift_range=0.1)
+datagen.fit(X_train)
 
-    # Fit the model on the batches generated by datagen.flow().
-model.fit_generator(datagen.flow(x_train, y_train,
+# Fit the model on the batches generated by datagen.flow().
+model.fit_generator(datagen.flow(X_train, y_train,
                                      batch_size=config.batch_size),
-                        steps_per_epoch=x_train.shape[0] // config.batch_size,
+                        steps_per_epoch=X_train.shape[0] // config.batch_size,
                         epochs=config.epochs,
-                        validation_data=(x_test, y_test),
+                        validation_data=(X_test, y_test),
                         workers=4,
-                        callbacks=[WandbKerasCallback(data_type="image", labels=class_names)]
-   )
+                        callbacks=[WandbCallback(data_type="image", labels=class_names)]
+)
 
 

keras-cifar/cifar-cnn.py

@@ -1,4 +1,4 @@
 [default]
 entity: qualcomm
-project: cifar-july27
+project: cifar-aug22
 base_url: https://api.wandb.ai

keras-cifar/config-defaults.yaml

@@ -1,4 +1,4 @@
 [default]
 entity: qualcomm
-project: digits-july30
+project: digits-sep12
 base_url: https://api.wandb.ai

keras-cifar/wandb/settings

@@ -36,23 +36,14 @@
 # create model
 model=Sequential()
 #model.add(Reshape((img_width, img_height, 1), input_shape=(img_width,img_height)))
-#model.add(Dropout(0.4))
-#model.add(Conv2D(32, (3,3), activation='relu'))
-#model.add(MaxPooling2D(2,2))
-#model.add(Dropout(0.4))
-#model.add(Conv2D(32, (3,3), activation='relu'))
-#model.add(MaxPooling2D(2,2))
 model.add(Flatten(input_shape=(img_width,img_height)))
 model.add(Dropout(0.4))
 model.add(Dense(100, activation='relu'))
 model.add(Dropout(0.4))
 model.add(Dense(num_classes, activation='softmax'))
 model.compile(loss='categorical_crossentropy', optimizer='adam',
                 metrics=['accuracy'])
-model.summary()
 # Fit the model
 model.fit(X_train, y_train, epochs=config.epochs, validation_data=(X_test, y_test),
-                    callbacks=[WandbCallback(validation_data=X_test, labels=labels)])
+                    callbacks=[WandbCallback(data_type="image", labels=labels)])
 
-#print("Predictions", model.predict(X_train[:50]))
-#print("Truth", y_train[:50])

keras-cnn/wandb/settings

@@ -31,16 +31,9 @@
 
 # create model
 model=Sequential()
-model.add(Reshape((28,28,1), input_shape=(28,28)))
-model.add(Dropout(0.5))
-model.add(Conv2D(32, (3,3), activation='relu'))
-model.add(Dropout(0.5))
-model.add(MaxPooling2D(pool_size=(2,2)))
-model.add(Flatten())
-model.add(Dense(100, activation='relu'))
-model.add(Dropout(0.5))
-model.add(Dense(num_classes, activation='softmax'))
-model.compile(loss='categorical_crossentropy', optimizer='adam',
+model.add(Flatten(input_shape=(img_width, img_height)))
+model.add(Dense(num_classes))
+model.compile(loss='mse', optimizer='adam',
                 metrics=['accuracy'])
 
 # Fit the model

keras-fashion/nn.py

@@ -1,4 +1,4 @@
 [default]
 entity: qualcomm
-project: fashion-july27
+project: fashion-sep12
 base_url: https://api.wandb.ai