i used unet model with 512x512x1 input shape but i have a problem. I am getting a ValueError : Dimensions must be equal. I know that something goes wrong with input shapes. i need solve this problem.maybe there is a problem with the loss of the model
X Train Shape : (512, 512, 1) Y Train Shape : (512, 512, 1)
Model:
from keras.models import Model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.layers import Input, merge, UpSampling2D,BatchNormalization
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K
import tensorflow as tf
K.set_image_data_format('channels_last')
def dice_coef(y_true, y_pred):
smooth = 0.005
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)
def unet_model():
inputs = Input((512 , 512, 1))
conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs)
batch1 = BatchNormalization(axis=1)(conv1)
conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1)
batch1 = BatchNormalization(axis=1)(conv1)
pool1 = MaxPooling2D((2, 2)) (batch1)
conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (pool1)
batch2 = BatchNormalization(axis=1)(conv2)
conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch2)
batch2 = BatchNormalization(axis=1)(conv2)
pool2 = MaxPooling2D((2, 2)) (batch2)
conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (pool2)
batch3 = BatchNormalization(axis=1)(conv3)
conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch3)
batch3 = BatchNormalization(axis=1)(conv3)
pool3 = MaxPooling2D((2, 2)) (batch3)
conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (pool3)
batch4 = BatchNormalization(axis=1)(conv4)
conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch4)
batch4 = BatchNormalization(axis=1)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2)) (batch4)
conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (pool4)
batch5 = BatchNormalization(axis=1)(conv5)
conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (batch5)
batch5 = BatchNormalization(axis=1)(conv5)
up6 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same') (batch5)
up6 = concatenate([up6, conv4], axis=1)
conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (up6)
batch6 = BatchNormalization(axis=1)(conv6)
conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch6)
batch6 = BatchNormalization(axis=1)(conv6)
up7 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same') (batch6)
up7 = concatenate([up7, conv3], axis=1)
conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (up7)
batch7 = BatchNormalization(axis=1)(conv7)
conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch7)
batch7 = BatchNormalization(axis=1)(conv7)
up8 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (batch7)
up8 = concatenate([up8, conv2], axis=1)
conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (up8)
batch8 = BatchNormalization(axis=1)(conv8)
conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch8)
batch8 = BatchNormalization(axis=1)(conv8)
up9 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (batch8)
up9 = concatenate([up9, conv1], axis=1)
conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (up9)
batch9 = BatchNormalization(axis=1)(conv9)
conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch9)
batch9 = BatchNormalization(axis=1)(conv9)
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(batch9)
model = Model(inputs=[inputs], outputs=[conv10])
model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])
return model
model = unet_model()
If you check the model summary (model.summary()) you will see that the last layer output shape is:
Layer (type) Output Shape Param # Connected to
===============================================================================
...
...
conv2d_18 (Conv2D) (None, 2560, 512, 1) 65 batch_normalization_17[0][0]
The output shape is (None,2560,512,1) which is not match with the desired output shape that is (None, 512, 512, 1)
This mismatch is because of your concatenate layers. You have set axis=1 parameter in the concatenate layers which concatenate second axis, but you need to concatenate layers based on last axis which is axis=-1 and the default.
So, Remove axis parameter from your concatenate layers to let it remain the default or set axis=-1 in all your concatenate layers.
For example:
#up6 = concatenate([up6, conv4], axis=1) # remove axis
up6 = concatenate([up6, conv4])
Then, you will get last layer shape the same as your input and your y_train:
Layer (type) Output Shape Param # Connected to
===============================================================================
...
...
conv2d_18 (Conv2D) (None, 512, 512, 1) 65 batch_normalization_17[0][0]