CNN检测模型统计检出率

X, y = get_feature_charseq()
#max_document_length=64
volcab_file = "volcab.pkl"
assert os.path.exists(volcab_file)
pkl_file = open(volcab_file, 'rb')
data = pickle.load(pkl_file)
valid_chars, max_document_length, max_features = data["valid_chars"], data["max_len"], data["volcab_size"]
 
print "max_features:", max_features
print "max_len:", max_document_length
 
model = get_cnn_model(max_document_length, max_features, is_training=False)
model.load(filename)
 
print "X:", X[:3]
print "Y:", y[:3]
print "len X:", len(X)
X = pad_sequences(X, maxlen=max_document_length, value=0.)
 
result = model.predict(X[1:4])
print result
 
cnt = 0
page = 10000
total_page = int((len(X)+page-1)/page)
print "total_page:", total_page
labels = y
labels_pred = []
for ii in range(0, total_page):
        predictions = model.predict(X[ii*page:ii*page+page])
 
        labels_pred += list(np.argmax(predictions, axis=1))
 
        for i,p in enumerate(predictions):
            i = i+ii*page
 
            if p[1] >= 0.95:
                cnt += 1
print cnt, " total black(0.95)"
print(confusion_matrix(labels, labels_pred))
print(classification_report(labels, labels_pred))