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llvm/projects/keras/src/mobilenet_cifar10.py

+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+from keras.models import Sequential
+from keras.layers import *
+from keras.datasets import cifar10
+from keras.utils import to_categorical
+from keras.callbacks import *
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Model
+from keras import optimizers
+import keras.backend as K
+
+
+(X_train, y_train), (X_test, y_test) = cifar10.load_data()
+
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+mean = np.mean(X_train, axis=(0, 1, 2), keepdims=True)
+std = np.std(X_train, axis=(0, 1, 2), keepdims=True)
+X_train = (X_train - mean) / (std + 1e-9)
+X_test = (X_test - mean) / (std + 1e-9)
+
+y_train = to_categorical(y_train, num_classes=10)
+y_test = to_categorical(y_test, num_classes=10)
+
+
+def get_mobilenet(alpha=1, depth_multiplier=1):
+    model = Sequential()
+    
+    def _conv_block(filters, alpha, kernel=(3, 3), strides=(1, 1)):
+        channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
+        filters = int(filters * alpha)
+        model.add(Conv2D(filters, kernel,
+                          padding='same',
+                          use_bias=False,
+                          strides=strides, 
+                        input_shape=(32, 32, 3)))
+        model.add(BatchNormalization(axis=channel_axis))
+        model.add(ReLU())
+    
+    def _depthwise_conv_block(pointwise_conv_filters, alpha, depth_multiplier=1, strides=(1, 1)):
+        channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
+        pointwise_conv_filters = int(pointwise_conv_filters * alpha)
+
+        model.add(DepthwiseConv2D((3, 3),
+                                   padding='same',
+                                   depth_multiplier=depth_multiplier,
+                                   strides=strides,
+                                   use_bias=False))    
+        model.add(BatchNormalization(axis=channel_axis))
+        model.add(ReLU())
+        model.add(Conv2D(pointwise_conv_filters, (1, 1),
+                          padding='same',
+                          use_bias=False,
+                          strides=(1, 1)))
+        model.add(BatchNormalization(axis=channel_axis))
+        model.add(ReLU())
+        
+
+    _conv_block(32, alpha, strides=(1, 1))
+    
+    _depthwise_conv_block(64, alpha, depth_multiplier)
+    
+    _depthwise_conv_block(128, alpha, depth_multiplier,
+                              strides=(2, 2))
+    _depthwise_conv_block(128, alpha, depth_multiplier)
+    model.add(Dropout(rate=0.5))
+
+    _depthwise_conv_block(256, alpha, depth_multiplier, 
+                      strides=(2, 2))
+    _depthwise_conv_block(256, alpha, depth_multiplier)
+    model.add(Dropout(rate=0.5))
+
+    _depthwise_conv_block(512, alpha, depth_multiplier,
+                      strides=(2, 2))
+    _depthwise_conv_block(512, alpha, depth_multiplier)
+    _depthwise_conv_block(512, alpha, depth_multiplier)
+    model.add(Dropout(rate=0.5))
+    
+    _depthwise_conv_block(512, alpha, depth_multiplier)
+    _depthwise_conv_block(512, alpha, depth_multiplier)
+    _depthwise_conv_block(512, alpha, depth_multiplier)
+    model.add(Dropout(rate=0.5))
+    
+    _depthwise_conv_block(1024, alpha, depth_multiplier,
+                         strides=(2, 2))
+    _depthwise_conv_block(1024, alpha, depth_multiplier)
+    model.add(Dropout(rate=0.5))
+
+    model.add(GlobalAveragePooling2D())
+    model.add(Dense(10, activation='softmax'))
+
+    return model
+    
+    
+# data augmentation, horizontal flips only
+datagen = ImageDataGenerator(
+        featurewise_center=False,
+        featurewise_std_normalization=False,
+        rotation_range=0.0,
+        width_shift_range=0.0,
+        height_shift_range=0.0,
+        vertical_flip=False,
+        horizontal_flip=True)
+datagen.fit(X_train)
+
+
+model = get_mobilenet()
+
+learning_rates=[]
+for i in range(5):
+    learning_rates.append(2e-2)
+for i in range(50-5):
+    learning_rates.append(1e-2)
+for i in range(100-50):
+    learning_rates.append(8e-3)
+for i in range(150-100):
+    learning_rates.append(4e-3)
+for i in range(200-150):
+    learning_rates.append(2e-3)
+for i in range(300-200):
+    learning_rates.append(1e-3)
+
+callbacks = [
+    LearningRateScheduler(lambda epoch: float(learning_rates[epoch]))
+]
+
+model.compile(optimizer=optimizers.SGD(lr=learning_rates[0], momentum=0.9, decay=0.0, nesterov=False), 
+                       loss='categorical_crossentropy', 
+                       metrics=['accuracy'])
+
+model.fit_generator(
+    datagen.flow(X_train, y_train, batch_size=128),
+    steps_per_epoch=int(np.ceil(50000 / 128)),
+    validation_data=(X_test, y_test),
+    epochs=300,
+    callbacks=callbacks
+)
+
+model.save_weights('mobilenet_cifar10.h5')
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