In this notebook, we train a CNN to classify images from the CIFAR-10 database.
import keras
from keras.datasets import cifar10
# load the pre-shuffled train and test data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
```How
Using TensorFlow backend.
### 2. Visualize the First 24 Training Images
```python
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
fig = plt.figure(figsize=(20,5))
for i in range(36):
ax = fig.add_subplot(3, 12, i + 1, xticks=[], yticks=[])
ax.imshow(np.squeeze(x_train[i]))
# rescale [0,255] --> [0,1]
x_train = x_train.astype('float32')/255
x_test = x_test.astype('float32')/255
from keras.utils import np_utils
# one-hot encode the labels
num_classes = len(np.unique(y_train))
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# break training set into training and validation sets
(x_train, x_valid) = x_train[5000:], x_train[:5000]
(y_train, y_valid) = y_train[5000:], y_train[:5000]
# print shape of training set
print('x_train shape:', x_train.shape)
# print number of training, validation, and test images
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print(x_valid.shape[0], 'validation samples')
x_train shape: (45000, 32, 32, 3)
45000 train samples
10000 test samples
5000 validation samples
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=3, padding='same', activation='relu',
input_shape=(32, 32, 3)))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=128, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(10, activation='softmax'))
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_13 (Conv2D) (None, 32, 32, 16) 448
_________________________________________________________________
max_pooling2d_13 (MaxPooling (None, 16, 16, 16) 0
_________________________________________________________________
conv2d_14 (Conv2D) (None, 16, 16, 32) 4640
_________________________________________________________________
max_pooling2d_14 (MaxPooling (None, 8, 8, 32) 0
_________________________________________________________________
conv2d_15 (Conv2D) (None, 8, 8, 64) 18496
_________________________________________________________________
max_pooling2d_15 (MaxPooling (None, 4, 4, 64) 0
_________________________________________________________________
conv2d_16 (Conv2D) (None, 4, 4, 128) 73856
_________________________________________________________________
max_pooling2d_16 (MaxPooling (None, 2, 2, 128) 0
_________________________________________________________________
dropout_7 (Dropout) (None, 2, 2, 128) 0
_________________________________________________________________
flatten_4 (Flatten) (None, 512) 0
_________________________________________________________________
dense_7 (Dense) (None, 256) 131328
_________________________________________________________________
dropout_8 (Dropout) (None, 256) 0
_________________________________________________________________
dense_8 (Dense) (None, 10) 2570
=================================================================
Total params: 231,338.0
Trainable params: 231,338.0
Non-trainable params: 0.0
_________________________________________________________________
# compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam',
metrics=['accuracy'])
How
from keras.callbacks import ModelCheckpoint
# train the model
checkpointer = ModelCheckpoint(filepath='model.weights.best.hdf5', verbose=1,
save_best_only=True)
hist = model.fit(x_train, y_train, batch_size=32, epochs=15,
validation_data=(x_valid, y_valid), callbacks=[checkpointer],
verbose=2, shuffle=True)
Train on 45000 samples, validate on 5000 samples
Epoch 1/15
Epoch 00000: val_loss improved from inf to 0.86286, saving model to model.weights.best.hdf5
67s - loss: 0.8959 - acc: 0.6962 - val_loss: 0.8629 - val_acc: 0.7140
Epoch 2/15
Epoch 00001: val_loss did not improve
67s - loss: 0.8065 - acc: 0.7232 - val_loss: 0.9271 - val_acc: 0.7000
Epoch 3/15
Epoch 00002: val_loss improved from 0.86286 to 0.84090, saving model to model.weights.best.hdf5
66s - loss: 0.7559 - acc: 0.7383 - val_loss: 0.8409 - val_acc: 0.7116
Epoch 4/15
Epoch 00003: val_loss improved from 0.84090 to 0.78299, saving model to model.weights.best.hdf5
66s - loss: 0.7184 - acc: 0.7494 - val_loss: 0.7830 - val_acc: 0.7384
Epoch 5/15
Epoch 00004: val_loss did not improve
66s - loss: 0.6893 - acc: 0.7604 - val_loss: 0.7959 - val_acc: 0.7274
Epoch 6/15
Epoch 00005: val_loss did not improve
66s - loss: 0.6586 - acc: 0.7705 - val_loss: 0.8175 - val_acc: 0.7264
Epoch 7/15
Epoch 00006: val_loss did not improve
67s - loss: 0.6346 - acc: 0.7774 - val_loss: 0.7837 - val_acc: 0.7416
Epoch 8/15
Epoch 00007: val_loss did not improve
67s - loss: 0.6126 - acc: 0.7846 - val_loss: 0.8139 - val_acc: 0.7322
Epoch 9/15
Epoch 00008: val_loss did not improve
66s - loss: 0.5896 - acc: 0.7937 - val_loss: 0.8309 - val_acc: 0.7312
Epoch 10/15
Epoch 00009: val_loss improved from 0.78299 to 0.75832, saving model to model.weights.best.hdf5
67s - loss: 0.5599 - acc: 0.8003 - val_loss: 0.7583 - val_acc: 0.7498
Epoch 11/15
Epoch 00010: val_loss did not improve
67s - loss: 0.5517 - acc: 0.8061 - val_loss: 0.7844 - val_acc: 0.7472
Epoch 12/15
Epoch 00011: val_loss did not improve
67s - loss: 0.5363 - acc: 0.8096 - val_loss: 0.8014 - val_acc: 0.7500
Epoch 13/15
Epoch 00012: val_loss did not improvestatic/images/cifar10/output_3_0.png
67s - loss: 0.5154 - acc: 0.8167 - val_loss: 0.8286 - val_acc: 0.7378
Epoch 14/15
Epoch 00013: val_loss did not improve
66s - loss: 0.5058 - acc: 0.8185 - val_loss: 0.7958 - val_acc: 0.7554
Epoch 15/15
Epoch 00014: val_loss did not improve
66s - loss: 0.4890 - acc: 0.8264 - val_loss: 0.7996 - val_acc: 0.7462
# load the weights that yielded the best validation accuracy
model.load_weights('model.weights.best.hdf5')
# evaluate and print test accuracy
score = model.evaluate(x_test, y_test, verbose=0)
print('\n', 'Test accuracy:', score[1])
Test accuracy: 0.7361
This may give you some insight into why the network is misclassifying certain objects.
# get predictions on the test set
y_hat = model.predict(x_test)
# define text labels (source: https://www.cs.toronto.edu/~kriz/cifar.html)
cifar10_labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
# plot a random sample of test images, their predicted labels, and ground truth
fig = plt.figure(figsize=(64, 64))
for i, idx in enumerate(np.random.choice(x_test.shape[0], size=16, replace=False)):
ax = fig.add_subplot(16, 1, i + 1, xticks=[], yticks=[])
ax.imshow(np.squeeze(x_test[idx]))
pred_idx = np.argmax(y_hat[idx])
true_idx = np.argmax(y_test[idx])
ax.set_title("{} ({})".format(cifar10_labels[pred_idx], cifar10_labels[true_idx]),
color=("green" if pred_idx == true_idx else "red"))