def train_classifier(fold, model_name, x_train, y_train, x_valid, y_valid, id_valid):
print(model_name)
x_train_act = x_train
x_val_act = x_valid
# x_train_act = x_train.astype(np.float32)
# for idx in range(len(x_train_act)):
# x_train_act[idx] = preprocess_input(x_train_act[idx])
# print(x_train_act.max())
# x_val_act = x_valid.astype(np.float32)
# for idx in range(len(x_val_act)):
# x_val_act[idx] = preprocess_input(x_val_act[idx])
inputshape = x_train_act.shape[1:]
PTModel, preprocess_input = get_model(model_name)
base_pretrained_model = PTModel(input_shape = inputshape,
include_top = False, weights = 'imagenet')
base_pretrained_model.trainable = False
from keras import models, layers
from keras.optimizers import Adam
img_in = layers.Input(inputshape, name='Image_RGB_In')
x = base_pretrained_model(img_in)
x = layers.Flatten(name='flatten')(x)
x = Dense(256)(x)
x = BatchActivate(x)
x = Dropout(0.5)(x)
x = Dense(64)(x)
x = BatchActivate(x)
x = Dropout(0.5)(x)
out_layer = layers.Dense(1, activation = 'sigmoid')(x)
class_model = models.Model(inputs = [img_in], outputs = [out_layer], name = 'full_model')
class_model.compile(optimizer = Adam(lr=0.01),
loss = 'binary_crossentropy',
metrics = ['binary_accuracy'])
batch_size = 32
base_name = '{}_{}'.format(model_name, fold)
save_model_name = '../model/classifier/{}.model'.format(base_name)
submission_file = '../result/classifier/{}.csv'.format(base_name)
oof_file = '../result/classifier/{}_oof.csv'.format(base_name)
print(save_model_name)
print(submission_file)
print(oof_file)
board = keras.callbacks.TensorBoard(log_dir='log/classifier/{}'.format(base_name),
histogram_freq=0, write_graph=True, write_images=False)
early_stopping = EarlyStopping(monitor='val_binary_accuracy', mode = 'max',patience=5, verbose=1)
model_checkpoint = ModelCheckpoint(save_model_name,monitor='val_binary_accuracy',
mode = 'max', save_best_only=True, verbose=1)
reduce_lr = ReduceLROnPlateau(monitor='val_binary_accuracy', mode = 'max',factor=0.5, patience=2, min_lr=0.00001, verbose=1)
epochs = 200
history = class_model.fit(x_train, y_train,
validation_data=[x_valid, y_valid],
epochs=epochs,
batch_size=batch_size,
callbacks=[board, early_stopping, reduce_lr, model_checkpoint],
verbose=1)
model = load_model(save_model_name)
oof = model.predict(x_valid)
df_oof = pd.DataFrame()
df_oof['id'] = id_valid
df_oof['target'] = oof
df_oof.to_csv(oof_file, index=False)
files = os.listdir('../input/test/images/')
x_test = np.array([(np.array(load_img("../input/test/images/{}".format(idx), grayscale = False))) for idx in files]).reshape(-1, img_size_target, img_size_target, 3)
preds_test = model.predict(x_test)
df_result = pd.DataFrame()
df_result['id'] = files
df_result['pre'] = preds_test.reshape(len(files))
df_result.to_csv(submission_file, index=False)
y_col='has_ship_vec',
target_size=IMG_SIZE,
color_mode='rgb',
batch_size=VALID_IMG_COUNT)) # one big batch
print(valid_x.shape, valid_y.shape)
# In[ ]:
t_x, t_y = next(train_gen)
print('x', t_x.shape, t_x.dtype, t_x.min(), t_x.max())
print('y', t_y.shape, t_y.dtype, t_y.min(), t_y.max())
# In[ ]:
base_pretrained_model = PTModel(input_shape=t_x.shape[1:],
include_top=False,
weights='imagenet')
base_pretrained_model.trainable = False
# In[ ]:
from keras import models, layers
from keras.optimizers import Adam
img_in = layers.Input(t_x.shape[1:], name='Image_RGB_In')
img_noise = layers.GaussianNoise(GAUSSIAN_NOISE)(img_in)
pt_features = base_pretrained_model(img_noise)
pt_depth = base_pretrained_model.get_output_shape_at(0)[-1]
bn_features = layers.BatchNormalization()(pt_features)
feature_dropout = layers.SpatialDropout2D(DROPOUT)(bn_features)
gmp_dr = layers.GlobalMaxPooling2D()(feature_dropout)
dr_steps = layers.Dropout(DROPOUT)(layers.Dense(DENSE_COUNT,
lrate = LearningRateScheduler(step_decay)
callbacks_list = [qwk, lrate]
# from keras.applications.inception_v3 import InceptionV3 as PTModel
# from keras.applications.inception_resnet_v2 import InceptionResNetV2 as PTModel
# from keras.applications.resnet import ResNet50 as PTModel
# from keras.applications.vgg19 import VGG19 as PTModel
from keras.applications.vgg16 import VGG16 as PTModel
from keras.layers import GlobalAveragePooling2D, Dense, Dropout, Flatten, Input, Conv2D, multiply, LocallyConnected2D, Lambda
from keras.layers import BatchNormalization
input_shape = (512, 512, 3)
in_lay = Input(shape=input_shape)
base_pretrained_model = PTModel(input_tensor=in_lay,
include_top=False,
weights='imagenet')
base_pretrained_model.trainable = False
# pt_depth = base_pretrained_model.get_output_shape_at(0)[-1]
pt_features = base_pretrained_model(in_lay)
bn_features = BatchNormalization()(pt_features)
# # here we do an attention mechanism to turn pixels in the GAP on an off
# attn_layer = Conv2D(64, kernel_size = (1,1), padding = 'same', activation = 'relu')(Dropout(0.5)(bn_features))
# attn_layer = Conv2D(16, kernel_size = (1,1), padding = 'same', activation = 'relu')(attn_layer)
# attn_layer = Conv2D(8, kernel_size = (1,1), padding = 'same', activation = 'relu')(attn_layer)
# attn_layer = Conv2D(1, kernel_size = (1,1), padding = 'valid',activation = 'sigmoid')(attn_layer)
# # fan it out to all of the channels
# up_c2_w = np.ones((1, 1, 1, pt_depth))
# up_c2 = Conv2D(pt_depth, kernel_size = (1,1), padding = 'same',activation = 'linear', use_bias = False, weights = [up_c2_w])