train.py

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator


model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Flatten())
model.add(Dense(units = 128, activation = 'relu'))
model.add(Dense(units = 1, activation = 'sigmoid'))

model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)
val_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('E:/Hen Disease Detection/dataset2/train',
                                                 target_size = (64, 64),
                                                 batch_size = 8,
                                                 class_mode = 'binary')
val_set = val_datagen.flow_from_directory('E:/Hen Disease Detection/dataset2/val',
                                            target_size = (64, 64),
                                            batch_size = 8,
                                            class_mode = 'binary')

model.fit_generator(training_set,
                         steps_per_epoch = 50,
                         epochs = 25,
                         validation_data = val_set,
                         validation_steps = 12)



model_json = model.to_json()
with open("model.json1", "w") as json_file:
    json_file.write(model_json)
model.save_weights("model1.h5")
print("Saved model to disk")