|
|
import tensorflow as tf |
|
|
from tensorflow.keras.models import Sequential |
|
|
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D |
|
|
from tensorflow.keras.layers import ZeroPadding2D |
|
|
from keras.layers.normalization import BatchNormalization |
|
|
from tensorflow.keras.preprocessing.image import ImageDataGenerator |
|
|
|
|
|
model = Sequential() |
|
|
|
|
|
model.add(Conv2D(64, (9, 9), input_shape=(100,100,3),padding='same')) |
|
|
model.add(BatchNormalization()) |
|
|
model.add(Activation('relu')) |
|
|
model.add(MaxPooling2D(pool_size=(2, 2))) |
|
|
|
|
|
model.add(Conv2D(128, (5, 5), padding='same')) |
|
|
model.add(BatchNormalization()) |
|
|
model.add(Activation('relu')) |
|
|
model.add(MaxPooling2D(pool_size=(2, 2))) |
|
|
|
|
|
model.add(ZeroPadding2D((1, 1))) |
|
|
model.add(Conv2D(256, (3, 3), padding='same')) |
|
|
model.add(BatchNormalization()) |
|
|
model.add(Activation('relu')) |
|
|
model.add(MaxPooling2D(pool_size=(2, 2))) |
|
|
|
|
|
model.add(ZeroPadding2D((1, 1))) |
|
|
model.add(Conv2D(512, (3, 3), padding='same')) |
|
|
model.add(BatchNormalization()) |
|
|
model.add(Activation('relu')) |
|
|
|
|
|
model.add(ZeroPadding2D((1, 1))) |
|
|
model.add(Conv2D(512, (3, 3), padding='same')) |
|
|
model.add(BatchNormalization()) |
|
|
model.add(Activation('relu')) |
|
|
model.add(MaxPooling2D(pool_size=(2, 2))) |
|
|
|
|
|
model.add(Flatten()) |
|
|
model.add(Dense(64)) |
|
|
model.add(BatchNormalization()) |
|
|
model.add(Activation('relu')) |
|
|
model.add(Dropout(0.5)) |
|
|
|
|
|
model.add(Dense(128)) |
|
|
model.add(BatchNormalization()) |
|
|
model.add(Activation('relu')) |
|
|
model.add(Dropout(0.5)) |
|
|
|
|
|
model.add(Dense(1)) |
|
|
model.add(BatchNormalization()) |
|
|
model.add(Activation('sigmoid')) |
|
|
|
|
|
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) |
|
|
|
|
|
train_datagen = ImageDataGenerator(rescale = 1./255, |
|
|
rotation_range=30, |
|
|
shear_range=0.2, |
|
|
zoom_range=[0.8, 1.2], |
|
|
horizontal_flip=True, |
|
|
|
|
|
fill_mode='nearest') |
|
|
|
|
|
test_datagen = ImageDataGenerator(rescale = 1./255) |
|
|
|
|
|
training_set = train_datagen.flow_from_directory('/content/drive/My Drive/data/train', |
|
|
target_size = (100, 100), |
|
|
batch_size = 64, |
|
|
class_mode = 'binary') |
|
|
|
|
|
test_set = test_datagen.flow_from_directory('/content/drive/My Drive/data/val', |
|
|
target_size = (100, 100), |
|
|
batch_size = 64, |
|
|
class_mode = 'binary') |
|
|
my_callbacks = [ |
|
|
|
|
|
tf.keras.callbacks.ReduceLROnPlateau(factor=0.1, patience=3, min_lr=0.00001, verbose=1), |
|
|
tf.keras.callbacks.ModelCheckpoint('my_model2.h5', |
|
|
verbose=1, save_best_only=True, save_weights_only=False) |
|
|
] |
|
|
|
|
|
model.fit(training_set, epochs=200, validation_data = test_set, callbacks=my_callbacks) |
|
|
|
|
|
model.save('my_model2.h5') |
|
|
|
