I used Dice Loss and binary_crossentropy whenever I train my model it shows very high train and validation accuracy but always prints out blank images. My masks are black and white binary images where 0 corresponds to black and 1 corresponds to white. In my output image, almost all pixels have value 0 please tell me where am I going wrong.
def train_generator():
while True:
for start in range(0, len(os.listdir('/gdrive/My Drive/Train/img/images/')), 16):
x_batch = np.empty((16,256,512,1),dtype=np.float32)
y_batch = np.empty((16,256,512,1),dtype=np.float32)
end = min(start + 16, len(os.listdir('/gdrive/My Drive/Train/img/images/')))
ids_train_batch_images =os.listdir('/gdrive/My Drive/Train/img/images/')[start:end]
ids_train_batch_mask =os.listdir('/gdrive/My Drive/Train/msk/mask/')[start:end]
for i,id in enumerate(ids_train_batch_images):
x_sample = cv2.imread('/gdrive/My Drive/Train/img/images/'+ids_train_batch_images[i])
y_sample = cv2.imread('/gdrive/My Drive/Train/msk/mask/'+ids_train_batch_mask[i])
x_sample=cv2.resize(x_sample,(512,256),interpolation = cv2.INTER_AREA)
y_sample=cv2.resize(y_sample,(512,256),interpolation = cv2.INTER_AREA)
x_sample=x_sample[:,:,0]
y_sample=y_sample[:,:,0]
x_sample=np.expand_dims(x_sample,axis=-1)
y_sample=np.expand_dims(y_sample,axis=-1)
x_batch[i]=x_sample
y_batch[i]=y_sample.astype(np.bool)
x_batch = np.array(x_batch, np.float32)/255.0
y_batch = np.array(y_batch, np.bool)
yield x_batch, y_batch
def val_generator():
while True:
for start in range(0, len(os.listdir('/gdrive/My Drive/Validation/img/images/')), 16):
x_batch = np.empty((16,256,512,1),dtype=np.float32)
y_batch = np.empty((16,256,512,1),dtype=np.float32)
end = min(start + 16, len(os.listdir('/gdrive/My Drive/Validation/img/images/')))
ids_train_batch_images =os.listdir('/gdrive/My Drive/Validation/img/images/')[start:end]
ids_train_batch_mask =os.listdir('/gdrive/My Drive/Validation/msk/mask/')[start:end]
for i,id in enumerate(ids_train_batch_images):
x_sample = cv2.imread('/gdrive/My Drive/Validation/img/images/'+ids_train_batch_images[i])
y_sample = cv2.imread('/gdrive/My Drive/Validation/msk/mask/'+ids_train_batch_mask[i])
x_sample=cv2.resize(x_sample,(512,256),interpolation = cv2.INTER_AREA)
y_sample=cv2.resize(y_sample,(512,256),interpolation = cv2.INTER_AREA)
x_sample=x_sample[:,:,0]
y_sample=y_sample[:,:,0]
x_sample=np.expand_dims(x_sample,axis=-1)
y_sample=np.expand_dims(y_sample,axis=-1)
x_batch[i]=x_sample
y_batch[i]=y_sample.astype(np.bool)
x_batch = np.array(x_batch, np.float32)/255.0
y_batch = np.array(y_batch, np.bool)
yield x_batch, y_batch
train_gen=train_generator()
val_gen=val_generator()
def unet():
inputs = tf.keras.layers.Input((256,512,1))
s = inputs
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(s)
c1 = tf.keras.layers.Dropout(0.3)(c1)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c1)
p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(p1)
c2 = tf.keras.layers.Dropout(0.3)(c2)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c2)
p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(p2)
c3 = tf.keras.layers.Dropout(0.3)(c3)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c3)
p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(p3)
c4 = tf.keras.layers.Dropout(0.3)(c4)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c4)
p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)
c6 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(p4)
c6 = tf.keras.layers.Dropout(0.3)(c6)
c6 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c6)
p6 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c6)
# c6 = tf.keras.layers.Conv2D(1024, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(p5)
# c6 = tf.keras.layers.Dropout(0.1)(c6)
# c6 = tf.keras.layers.Conv2D(1024, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c6)
# p6 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c6)
c7 = tf.keras.layers.Conv2D(512, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(p6)
c7 = tf.keras.layers.Dropout(0.3)(c7)
c7 = tf.keras.layers.Conv2D(512, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c7)
# u8 = tf.keras.layers.Conv2DTranspose(1024, (2, 2), strides=(2, 2), padding='same')(c7)
# u8 = tf.keras.layers.concatenate([u8, c6])
# c8 = tf.keras.layers.Conv2D(1024, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u8)
# c8 = tf.keras.layers.Dropout(0.1)(c8)
# c8 = tf.keras.layers.Conv2D(1024, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c8)
u9 = tf.keras.layers.Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(c7)
u9 = tf.keras.layers.concatenate([u9, c6])
c9 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u9)
c9 = tf.keras.layers.Dropout(0.3)(c9)
c9 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(c9)
u10 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c9)
u10 = tf.keras.layers.concatenate([u10, c4])
c10 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u10)
c10 = tf.keras.layers.Dropout(0.3)(c10)
c10 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c10)
u11 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c10)
u11 = tf.keras.layers.concatenate([u11, c3], axis=3)
c11 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u11)
c11 = tf.keras.layers.Dropout(0.3)(c11)
c11 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c11)
u12 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c11)
u12 = tf.keras.layers.concatenate([u12, c2], axis=3)
c12 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u12)
c12 = tf.keras.layers.Dropout(0.3)(c12)
c12 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c12)
u13 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c12)
u13 = tf.keras.layers.concatenate([u13, c1], axis=3)
c13 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u13)
c13 = tf.keras.layers.Dropout(0.3)(c13)
c13 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c13)
outputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid')(c13)
model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
return model
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_4 (InputLayer) [(None, 256, 512, 1) 0
__________________________________________________________________________________________________
conv2d_69 (Conv2D) (None, 256, 512, 16) 160 input_4[0][0]
__________________________________________________________________________________________________
dropout_33 (Dropout) (None, 256, 512, 16) 0 conv2d_69[0][0]
__________________________________________________________________________________________________
conv2d_70 (Conv2D) (None, 256, 512, 16) 2320 dropout_33[0][0]
__________________________________________________________________________________________________
max_pooling2d_15 (MaxPooling2D) (None, 128, 256, 16) 0 conv2d_70[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D) (None, 128, 256, 32) 4640 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
dropout_34 (Dropout) (None, 128, 256, 32) 0 conv2d_71[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D) (None, 128, 256, 32) 9248 dropout_34[0][0]
__________________________________________________________________________________________________
max_pooling2d_16 (MaxPooling2D) (None, 64, 128, 32) 0 conv2d_72[0][0]
__________________________________________________________________________________________________
conv2d_73 (Conv2D) (None, 64, 128, 64) 18496 max_pooling2d_16[0][0]
__________________________________________________________________________________________________
dropout_35 (Dropout) (None, 64, 128, 64) 0 conv2d_73[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D) (None, 64, 128, 64) 36928 dropout_35[0][0]
__________________________________________________________________________________________________
max_pooling2d_17 (MaxPooling2D) (None, 32, 64, 64) 0 conv2d_74[0][0]
__________________________________________________________________________________________________
conv2d_75 (Conv2D) (None, 32, 64, 128) 73856 max_pooling2d_17[0][0]
__________________________________________________________________________________________________
dropout_36 (Dropout) (None, 32, 64, 128) 0 conv2d_75[0][0]
__________________________________________________________________________________________________
conv2d_76 (Conv2D) (None, 32, 64, 128) 147584 dropout_36[0][0]
__________________________________________________________________________________________________
max_pooling2d_18 (MaxPooling2D) (None, 16, 32, 128) 0 conv2d_76[0][0]
__________________________________________________________________________________________________
conv2d_77 (Conv2D) (None, 16, 32, 256) 295168 max_pooling2d_18[0][0]
__________________________________________________________________________________________________
dropout_37 (Dropout) (None, 16, 32, 256) 0 conv2d_77[0][0]
__________________________________________________________________________________________________
conv2d_78 (Conv2D) (None, 16, 32, 256) 590080 dropout_37[0][0]
__________________________________________________________________________________________________
max_pooling2d_19 (MaxPooling2D) (None, 8, 16, 256) 0 conv2d_78[0][0]
__________________________________________________________________________________________________
conv2d_79 (Conv2D) (None, 8, 16, 512) 1180160 max_pooling2d_19[0][0]
__________________________________________________________________________________________________
dropout_38 (Dropout) (None, 8, 16, 512) 0 conv2d_79[0][0]
__________________________________________________________________________________________________
conv2d_80 (Conv2D) (None, 8, 16, 512) 2359808 dropout_38[0][0]
__________________________________________________________________________________________________
conv2d_transpose_15 (Conv2DTran (None, 16, 32, 256) 524544 conv2d_80[0][0]
__________________________________________________________________________________________________
concatenate_15 (Concatenate) (None, 16, 32, 512) 0 conv2d_transpose_15[0][0]
conv2d_78[0][0]
__________________________________________________________________________________________________
conv2d_81 (Conv2D) (None, 16, 32, 256) 1179904 concatenate_15[0][0]
__________________________________________________________________________________________________
dropout_39 (Dropout) (None, 16, 32, 256) 0 conv2d_81[0][0]
__________________________________________________________________________________________________
conv2d_82 (Conv2D) (None, 16, 32, 256) 590080 dropout_39[0][0]
__________________________________________________________________________________________________
conv2d_transpose_16 (Conv2DTran (None, 32, 64, 128) 131200 conv2d_82[0][0]
__________________________________________________________________________________________________
concatenate_16 (Concatenate) (None, 32, 64, 256) 0 conv2d_transpose_16[0][0]
conv2d_76[0][0]
__________________________________________________________________________________________________
conv2d_83 (Conv2D) (None, 32, 64, 128) 295040 concatenate_16[0][0]
__________________________________________________________________________________________________
dropout_40 (Dropout) (None, 32, 64, 128) 0 conv2d_83[0][0]
__________________________________________________________________________________________________
conv2d_84 (Conv2D) (None, 32, 64, 128) 147584 dropout_40[0][0]
__________________________________________________________________________________________________
conv2d_transpose_17 (Conv2DTran (None, 64, 128, 64) 32832 conv2d_84[0][0]
__________________________________________________________________________________________________
concatenate_17 (Concatenate) (None, 64, 128, 128) 0 conv2d_transpose_17[0][0]
conv2d_74[0][0]
__________________________________________________________________________________________________
conv2d_85 (Conv2D) (None, 64, 128, 64) 73792 concatenate_17[0][0]
__________________________________________________________________________________________________
dropout_41 (Dropout) (None, 64, 128, 64) 0 conv2d_85[0][0]
__________________________________________________________________________________________________
conv2d_86 (Conv2D) (None, 64, 128, 64) 36928 dropout_41[0][0]
__________________________________________________________________________________________________
conv2d_transpose_18 (Conv2DTran (None, 128, 256, 32) 8224 conv2d_86[0][0]
__________________________________________________________________________________________________
concatenate_18 (Concatenate) (None, 128, 256, 64) 0 conv2d_transpose_18[0][0]
conv2d_72[0][0]
__________________________________________________________________________________________________
conv2d_87 (Conv2D) (None, 128, 256, 32) 18464 concatenate_18[0][0]
__________________________________________________________________________________________________
dropout_42 (Dropout) (None, 128, 256, 32) 0 conv2d_87[0][0]
__________________________________________________________________________________________________
conv2d_88 (Conv2D) (None, 128, 256, 32) 9248 dropout_42[0][0]
__________________________________________________________________________________________________
conv2d_transpose_19 (Conv2DTran (None, 256, 512, 16) 2064 conv2d_88[0][0]
__________________________________________________________________________________________________
concatenate_19 (Concatenate) (None, 256, 512, 32) 0 conv2d_transpose_19[0][0]
conv2d_70[0][0]
__________________________________________________________________________________________________
conv2d_89 (Conv2D) (None, 256, 512, 16) 4624 concatenate_19[0][0]
__________________________________________________________________________________________________
dropout_43 (Dropout) (None, 256, 512, 16) 0 conv2d_89[0][0]
__________________________________________________________________________________________________
conv2d_90 (Conv2D) (None, 256, 512, 16) 2320 dropout_43[0][0]
__________________________________________________________________________________________________
conv2d_91 (Conv2D) (None, 256, 512, 1) 17 conv2d_90[0][0]
==================================================================================================
Total params: 7,775,313
Trainable params: 7,775,313
Non-trainable params: 0
_________________________________________________________
from keras import backend as K
def dice_coef(y_true, y_pred, smooth=1):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_coef_loss(y_true, y_pred):
return 1-dice_coef(y_true, y_pred)
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import CSVLogger
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.optimizers import Adam
NO_OF_TRAINING_IMAGES = len(os.listdir('/gdrive/My Drive/Train/img/images/'))
NO_OF_VAL_IMAGES = len(os.listdir('/gdrive/My Drive/Validation/img/images/'))
NO_OF_EPOCHS = 1
BATCH_SIZE = 32
filepath="weights-improvement-{epoch:02d}-{val_accuracy:.2f}.hdf5"
m = unet()
opt = Adam(lr=1E-5, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
m.compile(optimizer=opt,loss=dice_coef_loss, metrics=[dice_coef])
checkpoint = ModelCheckpoint(filepath, monitor=dice_coef_loss,
verbose=1, save_best_only=True, mode='min')
earlystopping = EarlyStopping(monitor = dice_coef_loss, verbose = 1,
min_delta = 0.01, patience = 1, mode ='min')
callbacks_list = [checkpoint,earlystopping]
results = m.fit_generator(train_gen, epochs=NO_OF_EPOCHS,
steps_per_epoch = (NO_OF_TRAINING_IMAGES//BATCH_SIZE),
validation_data=val_gen,
validation_steps=(NO_OF_VAL_IMAGES//BATCH_SIZE),
use_multiprocessing=False,
workers=1)
m.save('Model.h5')
418/418 [==============================] - 9828s 24s/step - loss: 0.0700 - dice_coef: 0.9300 - val_loss: 0.0299 - val_dice_coef: 0.9701
but wen i take the output everything is just blank. I am scaling up the output by multiplying it by 255 before visualizing and batch normalization is also off
