application_vnd.jupyter.stderr_output_1.png
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D

from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda,Flatten

from keras.models import Model,Sequential

from keras.optimizers import Adam

from keras.models import Model, Sequential
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda, Flatten
from keras.optimizers import Adam
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
def model_1():
    model = Sequential()
    model.add(Conv2D(60, (3, 3), input_shape=(512, 512, 1), activation='relu'))
    model.add(Conv2D(60, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(30, (3, 3), activation='relu'))
    model.add(Conv2D(30, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(500, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(activation='softmax'))
    model.compile(Adam(lr=0.0002), loss='binary_crossentropy', metrics=['accuracy'])
    return model
model = model_1()
print(model.summary())