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test_cifar10_cnn.py
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test_cifar10_cnn.py
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from __future__ import print_function
from keras.datasets import cifar10
from keras.models import Sequential
from keras.models import model_from_json
from keras import backend as K
import matplotlib.pyplot as plt
import theano
import json
batch_size = 128
nb_classes = 10
nb_epoch = 20
# input image dimensions
img_rows, img_cols = 32, 32
# the CIFAR10 images are RGB
img_channels = 3
# the data, shuffled and split between tran and test sets
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
if K.image_dim_ordering() == 'th':
X_test = X_test.reshape(X_test.shape[0], img_channels, img_rows, img_cols)
input_shape = (img_channels, img_rows, img_cols)
else:
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, img_channels)
input_shape = (img_rows, img_cols, img_channels)
X_test = X_test.astype('float32')
X_test /= 255
print(X_test.shape[0], 'test samples')
# Reconstruct model
model = model_from_json(open('cifar10_model_architecture.json').read())
model.load_weights('cifar10_model_weights.h5')
# Plot history
hist = json.loads(open('cifar10_model_history.json').read())
plt.figure('history')
plt.subplot(211)
plt.title('Loss over epochs')
plt.plot(hist['loss'],'r',label='loss')
plt.plot(hist['val_loss'], 'b',label='val_loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.subplot(212)
plt.title('Accuracy over epochs')
plt.plot(hist['acc'],'r',label='acc')
plt.plot(hist['val_acc'], 'b',label='val_acc')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.tight_layout()
plt.show()
# Get output of each layer
get_1st_layer_output = K.function([model.layers[0].input, K.learning_phase()], [model.layers[0].output])
get_2nd_layer_output = K.function([model.layers[0].input, K.learning_phase()], [model.layers[2].output])
get_3rd_layer_output = K.function([model.layers[0].input, K.learning_phase()], [model.layers[7].output])
get_last_layer_output = K.function([model.layers[0].input, K.learning_phase()], [model.layers[10].output])
print('X_test image shape:', X_test.shape)
layer_1_output = get_1st_layer_output([X_test,0])[0]
print('Print 1st layer output', layer_1_output.shape)
layer_2_output = get_2nd_layer_output([X_test,0])[0]
print('Print 2nd layer output', layer_2_output.shape)
layer_3_output = get_3rd_layer_output([X_test,0])[0]
print('Print 3rd layer output', layer_3_output.shape)
layer_last_output = get_last_layer_output([X_test,0])[0]
print('Print last layer output', layer_last_output.shape)
# Predict classes and probability
print('Predict classes')
classes = model.predict_classes(X_test, batch_size)
print('Predict probability')
proba = model.predict_proba(X_test, batch_size)
# Plot output of each layer
def plotvalue(index):
plt.figure('Input data and 1~4 layer output value of X_test[{idx}]'.format(idx=index), figsize=(12,9), dpi=100)
plt.subplot2grid((5,8),(0,0),rowspan=2,colspan=2)
plt.title('Input data')
plt.imshow(X_test[index].transpose(1,2,0))
plt.subplot2grid((5,8),(0,2))
plt.imshow(layer_1_output[index][0], cmap='bone', interpolation='nearest')
plt.subplot2grid((5,8),(0,3))
plt.imshow(layer_1_output[index][1], cmap='bone', interpolation='nearest')
plt.subplot2grid((5,8),(0,4))
plt.imshow(layer_1_output[index][2], cmap='bone', interpolation='nearest')
plt.subplot2grid((5,8),(1,2))
plt.imshow(layer_1_output[index][3], cmap='bone', interpolation='nearest')
plt.subplot2grid((5,8),(1,3))
plt.imshow(layer_1_output[index][4], cmap='bone', interpolation='nearest')
plt.subplot2grid((5,8),(0,5))
plt.imshow(layer_2_output[index][0], cmap='bone', interpolation='nearest')
plt.subplot2grid((5,8),(0,6))
plt.imshow(layer_2_output[index][1], cmap='bone', interpolation='nearest')
plt.subplot2grid((5,8),(0,7))
plt.imshow(layer_2_output[index][2], cmap='bone', interpolation='nearest')
plt.subplot2grid((5,8),(1,5))
plt.imshow(layer_2_output[index][3], cmap='bone', interpolation='nearest')
plt.subplot2grid((5,8),(1,6))
plt.imshow(layer_2_output[index][4], cmap='bone', interpolation='nearest')
plt.subplot2grid((5,8),(2,0),colspan=8)
plt.imshow(layer_3_output[index].reshape(1,layer_3_output.shape[1]), cmap='bone', interpolation='nearest')
plt.subplot2grid((5,8),(3,0),colspan=8)
plt.title('probability prediction')
plt.plot(proba[index],'r',label='probability')
plt.subplot2grid((5,8),(4,0),colspan=8)
plt.title('Last layer output={ans}, y_test={sol}'.format(ans=classes[index],sol=y_test[index]))
plt.imshow(layer_last_output[index].reshape(1,layer_last_output.shape[1]), cmap='bone', interpolation='nearest')
plt.tight_layout()
plt.show()
# Plot test data
plotvalue(0)
plotvalue(1)
plotvalue(2)