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C_prediction.py
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C_prediction.py
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import os
import numpy as np
import matplotlib.pyplot as plt
from keras.models import load_model
from keras import optimizers
from keras.losses import categorical_crossentropy
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
model = load_model('h5files/conv6_gap_dataaug_150epoches.h5')
optimizer = optimizers.Adadelta()
model.compile(optimizer=optimizer, loss=categorical_crossentropy,metrics=['accuracy'])
from cnn_utils import *
import gc
_, _, X_test_orig, Y_test_orig, classes = load_dataset()
#normlize the data into the [0,1]
X_test = X_test_orig / 255.
del X_test_orig
gc.collect()
Y_pred = model.predict(X_test)
y_pred = np.argmax(Y_pred,axis=1)
a=1
def plot_confusion_matrix(y_true, y_pred, classes,
normalize=False,
title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
# Compute confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Only use the labels that appear in the data
# temp = unique_labels(y_true, y_pred)
# classes = classes[temp]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
return ax
class_names = ['0','1','2','3','4','5']
y_test = Y_test_orig[0]
plot_confusion_matrix(y_test, y_pred, classes=class_names,
title='Confusion matrix, without normalization')
plt.show()