train half model in one time and other in another time I am using keras library to build the convolutional neural network model and tensorflow as a backend so what I wanted to do is that to train the model in one point time then save that model and again use that trained model to train itself to improve the accuracy. So is it possible then how should I do?
You can save a keras model during the training and then load it up again. To save a model you can do so using a json format, and then to save the weights you can do so using a callback as follows:

Let's make some random model,

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])


Then we can save the model that we created itself

# Save the model
model_json = model.to_json()
with open("weights/model.json", "w") as json_file:
    json_file.write(model_json)


And we can use a callback to save the weights at some given checkpoint. In this example they are saved each time we get better model performance.

# Save the weights using a checkpoint.
filepath="weights/weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]

epochs = 4
batch_size = 128
# Fit the model weights.
model.fit(x_train_reshaped, y_train_binary,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          callbacks=callbacks_list,
          validation_data=(x_test_reshaped, y_test_binary))


Then we can load the model and weights in the future using

# load model saved as a json and create model
json_file = open('weights/model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)

# load weights into new model
loaded_model.load_weights("weights/weights-improvement-04-0.99.hdf5")
print("Loaded model from disk")

# evaluate loaded model on test data
#loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
predictions = loaded_model.predict_classes(x_test_reshaped[0:10], verbose=0)
show_imgs(x_test[0:10])
print('Predicted classes: ', predictions)


Once you have the loaded weights, then you can resume training by once again calling the fit function

epochs = 4
batch_size = 128
# Fit the model weights.
loaded_model.fit(x_train_reshaped, y_train_binary,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          callbacks=callbacks_list,
          validation_data=(x_test_reshaped, y_test_binary))


This will perform 4 more epochs of training.