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CIFAR-10 image classification with Keras ConvNet
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''' | |
Cifar-10 classification | |
Original dataset and info: https://www.cs.toronto.edu/~kriz/cifar.html for more information | |
See: https://www.bonaccorso.eu/2016/08/06/cifar-10-image-classification-with-keras-convnet/ for further information | |
''' | |
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 | |
# 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')) | |
# 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=250, | |
validation_data=(X_test / 255.0, to_categorical(Y_test)), | |
callbacks=[EarlyStopping(min_delta=0.001, patience=3)]) | |
# Evaluate the model | |
scores = model.evaluate(X_test / 255.0, to_categorical(Y_test)) | |
print('Loss: %.3f' % scores[0]) | |
print('Accuracy: %.3f' % scores[1]) |
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50000/50000 [==============================] - 8s - loss: 2.0241 - acc: 0.2407 - val_loss: 1.7554 - val_acc: 0.3679 | |
Epoch 2/250 | |
50000/50000 [==============================] - 7s - loss: 1.6900 - acc: 0.3752 - val_loss: 1.5634 - val_acc: 0.4311 | |
Epoch 3/250 | |
50000/50000 [==============================] - 7s - loss: 1.5477 - acc: 0.4333 - val_loss: 1.4581 - val_acc: 0.4715 | |
Epoch 4/250 | |
50000/50000 [==============================] - 7s - loss: 1.4600 - acc: 0.4669 - val_loss: 1.3661 - val_acc: 0.5080 | |
Epoch 5/250 | |
50000/50000 [==============================] - 7s - loss: 1.3886 - acc: 0.4982 - val_loss: 1.2941 - val_acc: 0.5327 | |
Epoch 6/250 | |
50000/50000 [==============================] - 7s - loss: 1.3239 - acc: 0.5236 - val_loss: 1.2299 - val_acc: 0.5627 | |
Epoch 7/250 | |
50000/50000 [==============================] - 7s - loss: 1.2725 - acc: 0.5465 - val_loss: 1.1857 - val_acc: 0.5819 | |
Epoch 8/250 | |
50000/50000 [==============================] - 7s - loss: 1.2253 - acc: 0.5637 - val_loss: 1.1405 - val_acc: 0.5905 | |
Epoch 9/250 | |
50000/50000 [==============================] - 7s - loss: 1.1831 - acc: 0.5786 - val_loss: 1.1158 - val_acc: 0.6049 | |
Epoch 10/250 | |
50000/50000 [==============================] - 7s - loss: 1.1481 - acc: 0.5952 - val_loss: 1.0691 - val_acc: 0.6213 | |
Epoch 11/250 | |
50000/50000 [==============================] - 7s - loss: 1.1144 - acc: 0.6087 - val_loss: 1.0336 - val_acc: 0.6366 | |
Epoch 12/250 | |
50000/50000 [==============================] - 7s - loss: 1.0888 - acc: 0.6159 - val_loss: 1.0219 - val_acc: 0.6435 | |
Epoch 13/250 | |
50000/50000 [==============================] - 7s - loss: 1.0599 - acc: 0.6283 - val_loss: 1.0057 - val_acc: 0.6466 | |
Epoch 14/250 | |
50000/50000 [==============================] - 7s - loss: 1.0313 - acc: 0.6366 - val_loss: 0.9747 - val_acc: 0.6621 | |
Epoch 15/250 | |
50000/50000 [==============================] - 7s - loss: 1.0126 - acc: 0.6446 - val_loss: 0.9838 - val_acc: 0.6588 | |
Epoch 16/250 | |
50000/50000 [==============================] - 7s - loss: 0.9848 - acc: 0.6547 - val_loss: 0.9212 - val_acc: 0.6774 | |
Epoch 17/250 | |
50000/50000 [==============================] - 7s - loss: 0.9672 - acc: 0.6603 - val_loss: 0.9165 - val_acc: 0.6821 | |
Epoch 18/250 | |
50000/50000 [==============================] - 7s - loss: 0.9410 - acc: 0.6705 - val_loss: 0.8926 - val_acc: 0.6912 | |
Epoch 19/250 | |
50000/50000 [==============================] - 7s - loss: 0.9233 - acc: 0.6781 - val_loss: 0.8909 - val_acc: 0.6908 | |
Epoch 20/250 | |
50000/50000 [==============================] - 7s - loss: 0.9028 - acc: 0.6842 - val_loss: 0.8558 - val_acc: 0.7051 | |
Epoch 21/250 | |
50000/50000 [==============================] - 7s - loss: 0.8851 - acc: 0.6901 - val_loss: 0.8618 - val_acc: 0.7018 | |
Epoch 22/250 | |
50000/50000 [==============================] - 7s - loss: 0.8701 - acc: 0.6956 - val_loss: 0.8321 - val_acc: 0.7131 | |
Epoch 23/250 | |
50000/50000 [==============================] - 7s - loss: 0.8508 - acc: 0.7048 - val_loss: 0.8248 - val_acc: 0.7145 | |
Epoch 24/250 | |
50000/50000 [==============================] - 7s - loss: 0.8369 - acc: 0.7087 - val_loss: 0.8083 - val_acc: 0.7220 | |
Epoch 25/250 | |
50000/50000 [==============================] - 7s - loss: 0.8258 - acc: 0.7114 - val_loss: 0.8023 - val_acc: 0.7191 | |
Epoch 26/250 | |
50000/50000 [==============================] - 7s - loss: 0.8102 - acc: 0.7176 - val_loss: 0.7914 - val_acc: 0.7256 | |
Epoch 27/250 | |
50000/50000 [==============================] - 7s - loss: 0.7910 - acc: 0.7230 - val_loss: 0.7717 - val_acc: 0.7354 | |
Epoch 28/250 | |
50000/50000 [==============================] - 7s - loss: 0.7835 - acc: 0.7260 - val_loss: 0.7682 - val_acc: 0.7349 | |
Epoch 29/250 | |
50000/50000 [==============================] - 7s - loss: 0.7716 - acc: 0.7311 - val_loss: 0.7557 - val_acc: 0.7371 | |
Epoch 30/250 | |
50000/50000 [==============================] - 7s - loss: 0.7568 - acc: 0.7364 - val_loss: 0.7483 - val_acc: 0.7409 | |
Epoch 31/250 | |
50000/50000 [==============================] - 7s - loss: 0.7458 - acc: 0.7390 - val_loss: 0.7527 - val_acc: 0.7382 | |
Epoch 32/250 | |
50000/50000 [==============================] - 7s - loss: 0.7334 - acc: 0.7444 - val_loss: 0.7391 - val_acc: 0.7439 | |
Epoch 33/250 | |
50000/50000 [==============================] - 7s - loss: 0.7293 - acc: 0.7463 - val_loss: 0.7523 - val_acc: 0.7387 | |
Epoch 34/250 | |
50000/50000 [==============================] - 7s - loss: 0.7122 - acc: 0.7509 - val_loss: 0.7234 - val_acc: 0.7494 | |
Epoch 35/250 | |
50000/50000 [==============================] - 7s - loss: 0.7039 - acc: 0.7525 - val_loss: 0.7079 - val_acc: 0.7533 | |
Epoch 36/250 | |
50000/50000 [==============================] - 7s - loss: 0.6925 - acc: 0.7588 - val_loss: 0.7177 - val_acc: 0.7535 | |
Epoch 37/250 | |
50000/50000 [==============================] - 7s - loss: 0.6829 - acc: 0.7606 - val_loss: 0.6987 - val_acc: 0.7598 | |
Epoch 38/250 | |
50000/50000 [==============================] - 7s - loss: 0.6721 - acc: 0.7659 - val_loss: 0.6984 - val_acc: 0.7597 | |
Epoch 39/250 | |
50000/50000 [==============================] - 7s - loss: 0.6618 - acc: 0.7682 - val_loss: 0.6875 - val_acc: 0.7604 | |
Epoch 40/250 | |
50000/50000 [==============================] - 7s - loss: 0.6526 - acc: 0.7718 - val_loss: 0.6852 - val_acc: 0.7627 | |
Epoch 41/250 | |
50000/50000 [==============================] - 7s - loss: 0.6416 - acc: 0.7767 - val_loss: 0.6901 - val_acc: 0.7634 | |
Epoch 42/250 | |
50000/50000 [==============================] - 7s - loss: 0.6389 - acc: 0.7774 - val_loss: 0.6787 - val_acc: 0.7647 | |
Epoch 43/250 | |
50000/50000 [==============================] - 7s - loss: 0.6283 - acc: 0.7785 - val_loss: 0.6790 - val_acc: 0.7649 | |
Epoch 44/250 | |
50000/50000 [==============================] - 7s - loss: 0.6124 - acc: 0.7856 - val_loss: 0.6798 - val_acc: 0.7646 | |
Epoch 45/250 | |
50000/50000 [==============================] - 7s - loss: 0.6108 - acc: 0.7858 - val_loss: 0.6736 - val_acc: 0.7693 | |
Epoch 46/250 | |
50000/50000 [==============================] - 7s - loss: 0.5985 - acc: 0.7896 - val_loss: 0.6578 - val_acc: 0.7730 | |
Epoch 47/250 | |
50000/50000 [==============================] - 7s - loss: 0.5935 - acc: 0.7923 - val_loss: 0.6538 - val_acc: 0.7755 | |
Epoch 48/250 | |
50000/50000 [==============================] - 7s - loss: 0.5862 - acc: 0.7945 - val_loss: 0.6751 - val_acc: 0.7702 | |
Epoch 49/250 | |
50000/50000 [==============================] - 7s - loss: 0.5746 - acc: 0.7991 - val_loss: 0.6498 - val_acc: 0.7738 | |
Epoch 50/250 | |
50000/50000 [==============================] - 7s - loss: 0.5692 - acc: 0.8009 - val_loss: 0.6494 - val_acc: 0.7759 | |
Epoch 51/250 | |
50000/50000 [==============================] - 7s - loss: 0.5613 - acc: 0.8027 - val_loss: 0.6485 - val_acc: 0.7788 | |
Epoch 52/250 | |
50000/50000 [==============================] - 7s - loss: 0.5557 - acc: 0.8043 - val_loss: 0.6430 - val_acc: 0.7765 | |
Epoch 53/250 | |
50000/50000 [==============================] - 7s - loss: 0.5470 - acc: 0.8084 - val_loss: 0.6363 - val_acc: 0.7801 | |
Epoch 54/250 | |
50000/50000 [==============================] - 7s - loss: 0.5387 - acc: 0.8140 - val_loss: 0.6695 - val_acc: 0.7729 | |
Epoch 55/250 | |
50000/50000 [==============================] - 7s - loss: 0.5333 - acc: 0.8135 - val_loss: 0.6472 - val_acc: 0.7789 | |
Epoch 56/250 | |
50000/50000 [==============================] - 7s - loss: 0.5274 - acc: 0.8138 - val_loss: 0.6358 - val_acc: 0.7832 | |
Epoch 57/250 | |
50000/50000 [==============================] - 7s - loss: 0.5209 - acc: 0.8178 - val_loss: 0.6276 - val_acc: 0.7859 | |
Epoch 58/250 | |
50000/50000 [==============================] - 7s - loss: 0.5113 - acc: 0.8218 - val_loss: 0.6442 - val_acc: 0.7811 | |
Epoch 59/250 | |
50000/50000 [==============================] - 7s - loss: 0.5046 - acc: 0.8241 - val_loss: 0.6353 - val_acc: 0.7851 | |
Epoch 60/250 | |
50000/50000 [==============================] - 7s - loss: 0.5040 - acc: 0.8238 - val_loss: 0.6239 - val_acc: 0.7900 | |
Epoch 61/250 | |
50000/50000 [==============================] - 7s - loss: 0.4921 - acc: 0.8261 - val_loss: 0.6271 - val_acc: 0.7886 | |
Epoch 62/250 | |
50000/50000 [==============================] - 7s - loss: 0.4921 - acc: 0.8258 - val_loss: 0.6158 - val_acc: 0.7905 | |
Epoch 63/250 | |
50000/50000 [==============================] - 7s - loss: 0.4781 - acc: 0.8308 - val_loss: 0.6162 - val_acc: 0.7900 | |
Epoch 64/250 | |
50000/50000 [==============================] - 7s - loss: 0.4794 - acc: 0.8316 - val_loss: 0.6333 - val_acc: 0.7847 | |
Epoch 65/250 | |
50000/50000 [==============================] - 7s - loss: 0.4691 - acc: 0.8357 - val_loss: 0.6230 - val_acc: 0.7883 | |
Epoch 66/250 | |
50000/50000 [==============================] - 7s - loss: 0.4597 - acc: 0.8383 - val_loss: 0.6217 - val_acc: 0.7900 | |
9408/10000 [===========================>..] - ETA: 0s | |
Loss: 0.622 | |
Accuracy: 0.790 |
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