Trainiertes neuronales netz speichern und abrufen

[elian123]

Mein problem ist das ich das Training speichern und wieder zu einem späterem Zeitpunkt abrufen möchte ohne alles neu berechnen zu müssen.
from keras.layers import Convolution2D
from keras.models import Sequential
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator
from IPython.display import display
from PIL import Image
import numpy as np
from keras.preprocessing import image
from keras.models import model_from_json

classifier = Sequential()
classifier.add(Convolution2D(32,3,3, input_shape = (64,64,3), activation = "relu"))
classifier.add(MaxPooling2D(pool_size = (2,2)))
classifier.add(Flatten())
classifier.add(Dense(output_dim = 128, activation = "relu"))
classifier.add(Dense(output_dim = 1, activation = "sigmoid"))
classifier.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)

test_datagen = ImageDataGenerator(rescale=1./255)

training_set = train_datagen.flow_from_directory(
"dataset/training_set",
target_size=(64,64),
batch_size=32,
class_mode="binary")

test_set = train_datagen.flow_from_directory(
"dataset/test_set",
target_size=(64,64),
batch_size=32,
class_mode="binary")

classifier.fit_generator(
training_set,
steps_per_epoch=1000,
epochs=1,
validation_data=test_set,
validation_steps=800)






test_image = image.load_img("random1.jpg", target_size = (64,64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
training_set.class_indices
if result[0][0] >= 0.5:
prediction = "dog"
else:
prediction = "cat"

[ThomasL]

Man kann den Classifier/das Model speichern und laden.
Schaust du hier https://www.tensorflow.org/tutorials/ke ... ore_models