neuro_to_train.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
from keras.models import Model

img_width, img_height = 150, 150
train_data_dir = 'train'
validation_data_dir = 'validation'
nb_train_samples = 107
nb_validation_samples = 32
epochs = 5
batch_size = 16

base_model = applications.VGG16(weights='imagenet', include_top=False)

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(2, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)

for layer in base_model.layers:
    layer.trainable = False

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples)

model_json = model.to_json()
json_file = open("neuro.json", "w")
json_file.write(model_json)
json_file.close()
model.save_weights("neuro.h5")