def train_model():
        batch_size = 1
        training_imgs = np.lib.format.open_memmap(filename=os.path.join(data_path, 'data.npy'),mode='r+')
        training_masks = np.lib.format.open_memmap(filename=os.path.join(data_path, 'mask.npy'),mode='r+')

        dl_model = create_model()
        print(dl_model.summary())

        model_checkpoint = ModelCheckpoint(os.path.join(data_path,'mod_weight.hdf5'), monitor='loss',verbose=1, save_best_only=True)

        dl_model.fit_generator(generator(training_imgs, training_masks, batch_size), steps_per_epoch=(len(training_imgs)/batch_size), epochs=4,verbose=1,callbacks=[model_checkpoint])


 def generator(train_imgs, train_masks=None, batch_size=None):

# Create empty arrays to contain batch of features and labels#

        if train_masks is not None:
                train_imgs_batch = np.zeros((batch_size,y_to_res,x_to_res,bands))
                train_masks_batch = np.zeros((batch_size,y_to_res,x_to_res,1))
                #train_masks = np.expand_dims(train_masks,-1)

                while True:
                        for i in range(batch_size):
                                # choose random index in features

                                index= random.choice(range(len(train_imgs)))

                                train_imgs_batch[i] = train_imgs[index]
                                train_masks_batch[i] = train_masks[index]

                        yield train_imgs_batch, train_masks_batch

        else:
                rec_imgs_batch = np.zeros((batch_size,y_to_res,x_to_res,bands))
                while True:
                        for i in range(batch_size):
                                # choose random index in features

                                index= random.choice(range(len(train_imgs)))

                                rec_imgs_batch[i] = train_imgs[index]

                        yield rec_imgs_batch

def train_generator(train_images,train_masks,batch_size):
# def train_model_batches():
        #image_datagen = ImageDataGenerator(rotation_range=90.,   horizontal_flip=True,    vertical_flip=True,    rescale=1./255)
        data_gen_args=dict(rotation_range=90.,horizontal_flip=True,vertical_flip=True,rescale=1./255)
        #image_datagen = ImageDataGenerator(**data_gen_args)
        #mask_datagen = ImageDataGenerator(**data_gen_args)
        image_datagen = ImageDataGenerator()
        mask_datagen = ImageDataGenerator()
        #image_datagen = ImageDataGenerator()
# # Provide the same seed and keyword arguments to the fit and flow methods
        seed = 1
        image_datagen.fit(train_images, augment=True, seed=seed)
        mask_datagen.fit(train_masks, augment=True, seed=seed)
        #train_masks = np.expand_dims(train_masks,-1)
        image_generator = image_datagen.flow(train_images,batch_size=batch_size)
        mask_generator = mask_datagen.flow(train_masks,batch_size=batch_size)
        #image_generator = image_datagen.flow_from_directory('data/images',class_mode=None,seed=seed)
        #image_generator = image_datagen.flow()
        return zip(image_generator, mask_generator)
        #mask_generator = mask_datagen.flow_from_directory('data/masks',class_mode=None,seed=seed)

Epoch 00001: loss improved from inf to 0.01683, saving model to /home/ubuntu/deep_learn/client_data/mod_weight.hdf5
Epoch 2/4
7569/7569 [==============================] - 3394s 448ms/step - loss: 0.0049 - binary_crossentropy: 0.0027 - jaccard_coef_int: 0.9983

Epoch 00002: loss improved from 0.01683 to 0.00492, saving model to /home/ubuntu/deep_learn/client_data/mod_weight.hdf5
Epoch 3/4
7569/7569 [==============================] - 3394s 448ms/step - loss: 0.0049 - binary_crossentropy: 0.0026 - jaccard_coef_int: 0.9982

Epoch 00003: loss improved from 0.00492 to 0.00488, saving model to /home/ubuntu/deep_learn/client_data/mod_weight.hdf5
Epoch 4/4
7569/7569 [==============================] - 3394s 448ms/step - loss: 0.0074 - binary_crossentropy: 0.0042 - jaccard_coef_int: 0.9975

Epoch 00004: loss did not improve
Traceback (most recent call last):
  File "image_rec.py", line 291, in <module>
    train_model()
  File "image_rec.py", line 208, in train_model
    dl_model.fit_generator(train_generator(training_imgs,training_masks,batch_size),steps_per_epoch=1,epochs=1,workers=1)
  File "image_rec.py", line 274, in train_generator
    image_datagen.fit(train_images, augment=True, seed=seed)
  File "/home/ubuntu/pyvirt_test/local/lib/python2.7/site-packages/keras/preprocessing/image.py", line 753, in fit
    x = np.copy(x)
  File "/home/ubuntu/pyvirt_test/local/lib/python2.7/site-packages/numpy/lib/function_base.py", line 1505, in copy
    return array(a, order=order, copy=True)
MemoryError