Image of airplane, car, bicycle Deep learning source

south-man (32)in #deep-learning • 6 years ago

It is a source to sort pictures after learning google image of airplane, car, bicycle and learn by deep learning.
If you collect a lot of photos, you will get higher accuracy. It took a lot of time to sort out the pictures, so I learned about 400 pictures only, so the accuracy did not come out so much .

The feather hub has the same source.
https://github.com/llejo3/deep-learning/blob/master/vehicle_classification_learning.md

from google_images_download import google_images_download

response = google_images_download.googleimagesdownload()

When I import a Google photo as a search query, I need to delete some photos that do not match my query.

#arguments = {"keywords":"forsythia,cherry blossoms,magnolia,azalea,tulip",
arguments = {"keywords":"Planes, Bicycles, Cars",
             "limit":600,
             "print_urls":True,
             format: "jpg,png",
             "chromedriver" : "./chromedriver.exe",
             "exact_size":"200,200"}
paths = response.download(arguments)

from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D, Dropout
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam, RMSprop, SGD
from keras.callbacks import EarlyStopping, TensorBoard, ModelCheckpoint

train_datagen = ImageDataGenerator(
        rescale=1./255,
        rotation_range=40,      # 40도까지 회전
        width_shift_range=0.2,  # 20%까지 좌우 이동
        height_shift_range=0.2, # 20%까지 상하 이동
        shear_range=0.2,        # 20%까지 기울임
        zoom_range=0.2,         # 20%까지 확대
        horizontal_flip=True   # 좌우 뒤집기
)

train = train_datagen.flow_from_directory(
    'vehicles/train',
    target_size=(200, 200),
    #batch_size=32,
    class_mode='categorical')

valid = ImageDataGenerator(rescale=1.0/255).flow_from_directory(
    'vehicles/validation',
    target_size=(200, 200),
    #batch_size=32,
    class_mode='categorical',
    shuffle=False)

Found 884 images belonging to 3 classes.
Found 298 images belonging to 3 classes.

model1 = Sequential()
model1.add(Conv2D(32, (3, 3), activation='relu', input_shape=(200, 200, 3)))
model1.add(MaxPooling2D((2, 2)))
model1.add(Flatten())
model1.add(Dense(3, activation='softmax'))

model1.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_8 (Conv2D)            (None, 198, 198, 32)      896       
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 99, 99, 32)        0         
_________________________________________________________________
flatten_7 (Flatten)          (None, 313632)            0         
_________________________________________________________________
dense_7 (Dense)              (None, 3)                 940899    
=================================================================
Total params: 941,795
Trainable params: 941,795
Non-trainable params: 0
_________________________________________________________________

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model1.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=sgd)

history1 = model1.fit_generator(
    train, validation_data=valid, epochs=30,
    callbacks=[
        EarlyStopping(monitor = "val_loss", patience=2),
        TensorBoard(log_dir='tensorboard_logs/log_model1')
    ])

Epoch 1/30
 1/28 [>.............................] - ETA: 46s - loss: 1.1692 - acc: 0.3438

C:\Users\south\Anaconda3\lib\site-packages\PIL\Image.py:918: UserWarning: Palette images with Transparency   expressed in bytes should be converted to RGBA images
  'to RGBA images')


28/28 [==============================] - 36s 1s/step - loss: 3.7392 - acc: 0.3803 - val_loss: 1.0593 - val_acc: 0.4564
Epoch 2/30
28/28 [==============================] - 35s 1s/step - loss: 1.1686 - acc: 0.4723 - val_loss: 1.0099 - val_acc: 0.5839
Epoch 3/30
28/28 [==============================] - 36s 1s/step - loss: 0.9614 - acc: 0.5998 - val_loss: 0.9786 - val_acc: 0.5772
Epoch 4/30
28/28 [==============================] - 36s 1s/step - loss: 0.9075 - acc: 0.6209 - val_loss: 0.9295 - val_acc: 0.5805
Epoch 5/30
28/28 [==============================] - 36s 1s/step - loss: 0.8156 - acc: 0.6610 - val_loss: 0.8961 - val_acc: 0.6242
Epoch 6/30
28/28 [==============================] - 36s 1s/step - loss: 0.7874 - acc: 0.6738 - val_loss: 0.9063 - val_acc: 0.5973
Epoch 7/30
28/28 [==============================] - 37s 1s/step - loss: 0.7597 - acc: 0.6724 - val_loss: 0.8911 - val_acc: 0.6074
Epoch 8/30
28/28 [==============================] - 37s 1s/step - loss: 0.7685 - acc: 0.6638 - val_loss: 0.9621 - val_acc: 0.5772
Epoch 9/30
28/28 [==============================] - 34s 1s/step - loss: 0.7566 - acc: 0.6674 - val_loss: 0.8563 - val_acc: 0.6376
Epoch 10/30
28/28 [==============================] - 34s 1s/step - loss: 0.6990 - acc: 0.7087 - val_loss: 0.9243 - val_acc: 0.6074
Epoch 11/30
28/28 [==============================] - 35s 1s/step - loss: 0.7083 - acc: 0.7040 - val_loss: 0.9144 - val_acc: 0.6208

model2 = Sequential()
model2.add(Conv2D(32, (3, 3), activation='relu', input_shape=(200, 200, 3)))
model2.add(MaxPooling2D((2, 2)))
model2.add(Conv2D(32, (3, 3), activation='relu'))
model2.add(MaxPooling2D((2, 2)))
model2.add(Flatten())
model2.add(Dense(3, activation='softmax'))

model2.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_11 (Conv2D)           (None, 198, 198, 32)      896       
_________________________________________________________________
max_pooling2d_11 (MaxPooling (None, 99, 99, 32)        0         
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 97, 97, 32)        9248      
_________________________________________________________________
max_pooling2d_12 (MaxPooling (None, 48, 48, 32)        0         
_________________________________________________________________
flatten_9 (Flatten)          (None, 73728)             0         
_________________________________________________________________
dense_9 (Dense)              (None, 3)                 221187    
=================================================================
Total params: 231,331
Trainable params: 231,331
Non-trainable params: 0
_________________________________________________________________

sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
model2.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=sgd)

history2 = model2.fit_generator(
    train, validation_data=valid, epochs=100,
    callbacks=[
        EarlyStopping(monitor = "val_loss", patience=2),
        TensorBoard(log_dir='tensorboard_logs/log_model2')
    ])

Epoch 1/30
 4/28 [===>..........................] - ETA: 44s - loss: 1.4224 - acc: 0.3391

C:\Users\south\Anaconda3\lib\site-packages\PIL\Image.py:918: UserWarning: Palette images with Transparency   expressed in bytes should be converted to RGBA images
  'to RGBA images')


28/28 [==============================] - 57s 2s/step - loss: 1.1169 - acc: 0.4312 - val_loss: 1.3070 - val_acc: 0.3658
Epoch 2/30
28/28 [==============================] - 53s 2s/step - loss: 1.0442 - acc: 0.4781 - val_loss: 1.0760 - val_acc: 0.4262
Epoch 3/30
28/28 [==============================] - 55s 2s/step - loss: 0.9853 - acc: 0.5179 - val_loss: 0.9727 - val_acc: 0.5503
Epoch 4/30
28/28 [==============================] - 55s 2s/step - loss: 0.9722 - acc: 0.5405 - val_loss: 1.0100 - val_acc: 0.6107
Epoch 5/30
28/28 [==============================] - 54s 2s/step - loss: 0.9162 - acc: 0.5844 - val_loss: 0.9376 - val_acc: 0.6040
Epoch 6/30
28/28 [==============================] - 59s 2s/step - loss: 0.7842 - acc: 0.6691 - val_loss: 1.0358 - val_acc: 0.6107
Epoch 7/30
28/28 [==============================] - 55s 2s/step - loss: 0.7229 - acc: 0.6908 - val_loss: 0.7254 - val_acc: 0.7148
Epoch 8/30
28/28 [==============================] - 55s 2s/step - loss: 0.7381 - acc: 0.6953 - val_loss: 0.8614 - val_acc: 0.6342
Epoch 9/30
28/28 [==============================] - 54s 2s/step - loss: 0.6778 - acc: 0.7174 - val_loss: 0.7262 - val_acc: 0.6980

model3 = Sequential()
model3.add(Conv2D(32, (3, 3), activation='relu', input_shape=(200, 200, 3)))
model3.add(BatchNormalization())
model3.add(Conv2D(32, (3, 3), activation='relu'))
model3.add(MaxPooling2D(pool_size=(2, 2)))
model3.add(Dropout(0.25))

model3.add(Conv2D(64, (3, 3), activation='relu'))
model3.add(BatchNormalization())
model3.add(Conv2D(64, (3, 3), activation='relu'))
model3.add(MaxPooling2D(pool_size=(2, 2)))
model3.add(Dropout(0.25))

model3.add(Flatten())
model3.add(Dense(256, activation='relu'))
model3.add(Dropout(0.5))
model3.add(Dense(3, activation='softmax'))

model3.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_19 (Conv2D)           (None, 198, 198, 32)      896       
_________________________________________________________________
max_pooling2d_16 (MaxPooling (None, 99, 99, 32)        0         
_________________________________________________________________
dropout_4 (Dropout)          (None, 99, 99, 32)        0         
_________________________________________________________________
conv2d_20 (Conv2D)           (None, 97, 97, 64)        18496     
_________________________________________________________________
max_pooling2d_17 (MaxPooling (None, 48, 48, 64)        0         
_________________________________________________________________
dropout_5 (Dropout)          (None, 48, 48, 64)        0         
_________________________________________________________________
flatten_11 (Flatten)         (None, 147456)            0         
_________________________________________________________________
dense_12 (Dense)             (None, 256)               37748992  
_________________________________________________________________
dropout_6 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_13 (Dense)             (None, 3)                 771       
=================================================================
Total params: 37,769,155
Trainable params: 37,769,155
Non-trainable params: 0
_________________________________________________________________

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model3.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=sgd)

history3 = model3.fit_generator(
    train, validation_data=valid, epochs=30,
    callbacks=[
        EarlyStopping(monitor = "val_loss", patience=2),
        ModelCheckpoint('model3-{epoch:02d}.hdf5', save_best_only=True),
        TensorBoard(log_dir='tensorboard_logs/log_model3')
    ])

Epoch 1/30
 1/28 [>.............................] - ETA: 1:34 - loss: 1.1490 - acc: 0.2500

C:\Users\south\Anaconda3\lib\site-packages\PIL\Image.py:918: UserWarning: Palette images with Transparency   expressed in bytes should be converted to RGBA images
  'to RGBA images')


28/28 [==============================] - 85s 3s/step - loss: 1.2178 - acc: 0.3502 - val_loss: 1.0796 - val_acc: 0.3893
Epoch 2/30
28/28 [==============================] - 86s 3s/step - loss: 1.0463 - acc: 0.4851 - val_loss: 1.0564 - val_acc: 0.4899
Epoch 3/30
28/28 [==============================] - 82s 3s/step - loss: 0.9830 - acc: 0.5198 - val_loss: 1.0233 - val_acc: 0.5839
Epoch 4/30
28/28 [==============================] - 86s 3s/step - loss: 0.8876 - acc: 0.6224 - val_loss: 0.9437 - val_acc: 0.5336
Epoch 5/30
28/28 [==============================] - 84s 3s/step - loss: 0.7544 - acc: 0.6600 - val_loss: 0.8263 - val_acc: 0.6577
Epoch 6/30
28/28 [==============================] - 86s 3s/step - loss: 0.7302 - acc: 0.7052 - val_loss: 0.7034 - val_acc: 0.6879
Epoch 7/30
28/28 [==============================] - 89s 3s/step - loss: 0.6840 - acc: 0.7004 - val_loss: 0.6810 - val_acc: 0.7047
Epoch 8/30
28/28 [==============================] - 89s 3s/step - loss: 0.6499 - acc: 0.7214 - val_loss: 0.6697 - val_acc: 0.7248
Epoch 9/30
28/28 [==============================] - 87s 3s/step - loss: 0.6163 - acc: 0.7403 - val_loss: 0.6561 - val_acc: 0.7383
Epoch 10/30
28/28 [==============================] - 107s 4s/step - loss: 0.6387 - acc: 0.7238 - val_loss: 0.6992 - val_acc: 0.6846
Epoch 11/30
28/28 [==============================] - 90s 3s/step - loss: 0.5976 - acc: 0.7457 - val_loss: 0.6568 - val_acc: 0.7114