Image_Recognition_Trainer.py

from keras.datasets import cifar10

(X_train, y_train), (X_test, y_test) = cifar10.load_data()

from keras.utils import np_utils
new_X_train = X_train.astype('float32')
new_X_test = X_test.astype('float32')
new_X_train /= 255
new_X_test /= 255
new_Y_train = np_utils.to_categorical(y_train)
new_Y_test = np_utils.to_categorical(y_test)

from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.optimizers import SGD
from keras.constraints import maxnorm

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(32, 32, 3), activation='relu', padding='same', kernel_constraint=maxnorm(3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01), metrics=['accuracy'])
model.fit(new_X_train, new_Y_train, epochs=1, batch_size=32)

import h5py
model.save('Trained_model.h5')