train_detect.py

from __future__ import print_function

import numpy as np

from keras.callbacks import EarlyStopping
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D
from keras.optimizers import Adam
from keras.layers.pooling import MaxPooling2D
from keras.utils import to_categorical
from keras.callbacks import CSVLogger
# For reproducibility
np.random.seed(1000)

if __name__ == '__main__':
    # Load the dataset
    (X_train, Y_train), (X_test, Y_test) = cifar10.load_data()

    # Create the model
    model = Sequential()

    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)))
    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(1024, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(10, activation='softmax'))
    csv=CSVLogger("epochs1.log")

    # Compile the model
    model.compile(loss='categorical_crossentropy',
                  optimizer=Adam(lr=0.0001, decay=1e-6),
                  metrics=['accuracy'])

    # Train the model
    model.fit(X_train / 255.0, to_categorical(Y_train),
              batch_size=128,
              shuffle=True,
              epochs=63,
              validation_data=(X_test / 255.0, to_categorical(Y_test)),
              callbacks=[csv])

    # Evaluate the model
    scores = model.evaluate(X_test / 255.0, to_categorical(Y_test))

    print('Loss: %.3f' % scores[0])
    print('Accuracy: %.3f' % scores[1])
    
    import h5py
    model.save('cifar-10.h5')