Remove deprecated example, fix conv filter example

examples/cifar10_cnn_tfaugment2d.py

-'''
-#Train a simple deep CNN on the CIFAR10 small images dataset using augmentation.
-
-Using TensorFlow internal augmentation APIs by replacing ImageGenerator with
-an embedded AugmentLayer using LambdaLayer, which is faster on GPU.
-
-** Benchmark of `ImageGenerator`(IG) vs `AugmentLayer`(AL) both using augmentation
-2D:**
-
-(backend = Tensorflow-GPU, Nvidia Tesla P100-SXM2)
-
-Epoch no. | IG %Accuracy   | IG Performance | AL %Accuracy  | AL Performance
----------:|---------------:|---------------:|--------------:|--------------:
-1         | 44.84          | 15 ms/step     | 45.54         | 358 us/step
-2         | 52.34          |  8 ms/step     | 50.55         | 285 us/step
-8         | 65.45          |  8 ms/step     | 65.59         | 281 us/step
-25        | 76.74          |  8 ms/step     | 76.17         | 280 us/step
-100       | 78.81          |  8 ms/step     | 78.70         | 285 us/step
-
-Settings: horizontal_flip = True
-
-
-Epoch no. | IG %Accuracy   | IG Performance | AL %Accuracy  | AL Performance
----------:|---------------:|---------------:|--------------:|--------------:
-1         | 43.46          | 15 ms/step     | 42.21         | 334 us/step
-2         | 48.95          | 11 ms/step     | 48.06         | 282 us/step
-8         | 63.59          | 11 ms/step     | 61.35         | 290 us/step
-25        | 72.25          | 12 ms/step     | 71.08         | 287 us/step
-100       | 76.35          | 11 ms/step     | 74.62         | 286 us/step
-
-Settings: rotation = 30.0
-
-
-(Corner process and rotation precision by `ImageGenerator` and `AugmentLayer`
-are slightly different.)
-'''
-
-from __future__ import print_function
-import keras
-from keras.datasets import cifar10
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Conv2D, Lambda, MaxPooling2D
-from keras import backend as K
-import os
-
-if K.backend() != 'tensorflow':
-    raise RuntimeError('This example can only run with the '
-                       'TensorFlow backend, '
-                       'because it requires TF-native augmentation APIs')
-
-import tensorflow as tf
-
-
-def augment_2d(inputs, rotation=0, horizontal_flip=False, vertical_flip=False):
-    """Apply additive augmentation on 2D data.
-
-    # Arguments
-      rotation: A float, the degree range for rotation (0 <= rotation < 180),
-          e.g. 3 for random image rotation between (-3.0, 3.0).
-      horizontal_flip: A boolean, whether to allow random horizontal flip,
-          e.g. true for 50% possibility to flip image horizontally.
-      vertical_flip: A boolean, whether to allow random vertical flip,
-          e.g. true for 50% possibility to flip image vertically.
-
-    # Returns
-      input data after augmentation, whose shape is the same as its original.
-    """
-    if inputs.dtype != tf.float32:
-        inputs = tf.image.convert_image_dtype(inputs, dtype=tf.float32)
-
-    with tf.name_scope('augmentation'):
-        shp = tf.shape(inputs)
-        batch_size, height, width = shp[0], shp[1], shp[2]
-        width = tf.cast(width, tf.float32)
-        height = tf.cast(height, tf.float32)
-
-        transforms = []
-        identity = tf.constant([1, 0, 0, 0, 1, 0, 0, 0], dtype=tf.float32)
-
-        if rotation > 0:
-            angle_rad = rotation * 3.141592653589793 / 180.0
-            angles = tf.random_uniform([batch_size], -angle_rad, angle_rad)
-            f = tf.contrib.image.angles_to_projective_transforms(angles,
-                                                                 height, width)
-            transforms.append(f)
-
-        if horizontal_flip:
-            coin = tf.less(tf.random_uniform([batch_size], 0, 1.0), 0.5)
-            shape = [-1., 0., width, 0., 1., 0., 0., 0.]
-            flip_transform = tf.convert_to_tensor(shape, dtype=tf.float32)
-            flip = tf.tile(tf.expand_dims(flip_transform, 0), [batch_size, 1])
-            noflip = tf.tile(tf.expand_dims(identity, 0), [batch_size, 1])
-            transforms.append(tf.where(coin, flip, noflip))
-
-        if vertical_flip:
-            coin = tf.less(tf.random_uniform([batch_size], 0, 1.0), 0.5)
-            shape = [1., 0., 0., 0., -1., height, 0., 0.]
-            flip_transform = tf.convert_to_tensor(shape, dtype=tf.float32)
-            flip = tf.tile(tf.expand_dims(flip_transform, 0), [batch_size, 1])
-            noflip = tf.tile(tf.expand_dims(identity, 0), [batch_size, 1])
-            transforms.append(tf.where(coin, flip, noflip))
-
-    if transforms:
-        f = tf.contrib.image.compose_transforms(*transforms)
-        inputs = tf.contrib.image.transform(inputs, f, interpolation='BILINEAR')
-    return inputs
-
-
-batch_size = 32
-num_classes = 10
-epochs = 100
-num_predictions = 20
-save_dir = '/tmp/saved_models'
-model_name = 'keras_cifar10_trained_model.h5'
-
-# The data, split between train and test sets:
-(x_train, y_train), (x_test, y_test) = cifar10.load_data()
-print('x_train shape:', x_train.shape)
-print(x_train.shape[0], 'train samples')
-print(x_test.shape[0], 'test samples')
-
-# 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(Lambda(augment_2d,
-                 input_shape=x_train.shape[1:],
-                 arguments={'rotation': 8.0, 'horizontal_flip': True}))
-model.add(Conv2D(32, (3, 3), padding='same'))
-model.add(Activation('relu'))
-model.add(Conv2D(32, (3, 3)))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Dropout(0.25))
-
-model.add(Conv2D(64, (3, 3), padding='same'))
-model.add(Activation('relu'))
-model.add(Conv2D(64, (3, 3)))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Dropout(0.25))
-
-model.add(Flatten())
-model.add(Dense(512))
-model.add(Activation('relu'))
-model.add(Dropout(0.5))
-model.add(Dense(num_classes))
-model.add(Activation('softmax'))
-
-# initiate RMSprop optimizer
-opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
-
-# 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
-
-model.fit(x_train, y_train,
-          batch_size=batch_size,
-          epochs=epochs,
-          validation_data=(x_test, y_test),
-          shuffle=True)
-
-# Save model and weights
-if not os.path.isdir(save_dir):
-    os.makedirs(save_dir)
-model_path = os.path.join(save_dir, model_name)
-model.save(model_path)
-print('Saved trained model at %s ' % model_path)
-
-# Score trained model.
-scores = model.evaluate(x_test, y_test, verbose=1)
-print('Test loss:', scores[0])
-print('Test accuracy:', scores[1])

examples/conv_filter_visualization.py

@@ -164,7 +164,8 @@ def visualize_layer(model,
             img = deprocess_image(input_img_data[0])
             img = np.array(pil_image.fromarray(img).resize(intermediate_dim,
                                                            pil_image.BICUBIC))
-            input_img_data = [process_image(img, input_img_data[0])]
+            input_img_data = np.expand_dims(
+                process_image(img, input_img_data[0]), 0)
 
         # decode the resulting input image
         img = deprocess_image(input_img_data[0])