Ver 2
from keras.models import Sequential from keras.layers import Conv2D, Dense, MaxPool2D, Flatten, Dropout from tensorflow.keras import optimizers from tensorflow.keras.preprocessing.image import ImageDataGenerator import datetime # バッチサイズ batch_size = 32 # 画像分類したい数 num_classes = 3 # エポック数 epochs = 30 train_dir = 'images/train' validation_dir = 'images/test' # 画像の解像度 # image_height, image_weight = 1280, 720 image_height, image_weight = 384, 216 # CNN model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(image_height, image_weight, 3))) model.add(MaxPool2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPool2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPool2D((2, 2))) model.add(Conv2D(128, (3, 3), activation='relu')) model.add(MaxPool2D((2, 2))) model.add(Flatten()) model.add(Dropout(0.5)) model.add(Dense(512, activation='relu')) # 値が多いほうが精度はいいが計算量は増える model.add(Dense(num_classes, activation='softmax')) # 多分類の場合softmax model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc']) # 画像のRGBの値域が0-255であるのを0-1に正規化 train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=20, width_shift_range=10, height_shift_range=10, brightness_range=[0.7, 2] ) validation_datagen = ImageDataGenerator( rescale=1./255, rotation_range=20, width_shift_range=10, height_shift_range=10, brightness_range=[0.7, 2] ) train_generator = train_datagen.flow_from_directory( train_dir, target_size=(image_height, image_weight), batch_size=batch_size, class_mode='categorical' ) validation_generator = validation_datagen.flow_from_directory( validation_dir, target_size=(image_height, image_weight), batch_size=batch_size, class_mode='categorical' ) # log_dir="works/logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M") # tensorboard_callback = tf.keras.callbacks.TensorBoard( # log_dir=log_dir, # histogram_freq=1, # write_grads=True, # write_graph=False) from keras.callbacks import ModelCheckpoint mc = ModelCheckpoint( filepath='best.h5', monitor="val_loss", verbose=1, save_weights_only=False, mode='min', save_best_only=True, period=1 ) train_step_size = train_generator.n // train_generator.batch_size validation_step_size = validation_generator.n // validation_generator.batch_size model.fit_generator( train_generator, steps_per_epoch=train_step_size, epochs=epochs, validation_data=validation_generator, validation_steps=validation_step_size, verbose=1, callbacks=[mc] )
Ver 1
データは Fruits 360 dataset | Kaggle
# kerasでやろうとしていたがどうもtensorflowに吸収されたっぽい # 参考: https://book.mynavi.jp/manatee/detail/id=79420 # よって tensorflow.keras と奇妙に見えるがこれで正しい import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras import models from tensorflow.keras import optimizers from tensorflow.keras.preprocessing.image import ImageDataGenerator import datetime # GPUのメモリを使い切らないようにするおまじない # ちなみにTF2.xではエラーになるので注意 from keras.backend.tensorflow_backend import set_session config = tf.ConfigProto( gpu_options=tf.GPUOptions( visible_device_list="0", allow_growth=True ) ) set_session(tf.Session(config=config)) # バッチサイズ batch_size = 32 # 画像分類したい数 num_classes = 103 # エポック数 epochs = 20 # 画像の解像度 100x100pxとなる img_rows, img_cols = 100, 100 # CNN model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_rows, img_cols, 3))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Flatten()) model.add(layers.Dropout(0.5)) model.add(layers.Dense(512, activation='relu')) # 最後のsoftmaxは多分類の場合 model.add(layers.Dense(num_classes, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc']) # the data, split between train and test sets train_dir = 'works/data/fruits-360/Training' validation_dir = 'works/data/fruits-360/Test' # rescaleで正規化 train_datagen = ImageDataGenerator(rescale=1./255) validation_datagen = ImageDataGenerator(rescale=1./255) train_generator = train_datagen.flow_from_directory( train_dir, target_size=(img_rows, img_cols), batch_size=batch_size, class_mode='categorical' ) validation_generator = validation_datagen.flow_from_directory( validation_dir, target_size=(img_rows, img_cols), batch_size=batch_size, class_mode='categorical' ) log_dir="works/logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M") tensorboard_callback = tf.keras.callbacks.TensorBoard( log_dir=log_dir, histogram_freq=1, write_grads=True, write_graph=False) train_step_size = train_generator.n // train_generator.batch_size validation_step_size = validation_generator.n // validation_generator.batch_size model.fit_generator( train_generator, steps_per_epoch=train_step_size, epochs=epochs, validation_data=validation_generator, validation_steps=validation_step_size, verbose=1, callbacks=[tensorboard_callback] ) model.save('fruit_1.h5')
