simple_cnn.py

import tensorflow as tf
import keras
from tensorflow.python.keras.datasets import cifar10
from tensorflow.python.keras.utils import to_categorical
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
from tensorflow.python.keras.callbacks import TensorBoard


(x_train, y_train), (x_test, y_test) = cifar10.load_data()

x_train = x_train/255
x_test = x_test/255

y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

model = Sequential()

model.add(Conv2D(filters=32, input_shape=(32,32,3), kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'))
model.add(Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'))
model.add(Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(units=512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(units=10, activation='softmax'))

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

tsb = TensorBoard(log_dir='./logs')

history_model1 = model.fit(
    x_train,
    y_train,
    batch_size=32,
    epochs=20,
    validation_split=0.2,
    callbacks=[tsb]
)