eval_cnn.py

import os

import numpy as np
from mxnet import nd
from mxnet.gluon import nn
from sklearn.metrics import precision_recall_fscore_support, classification_report

CWD = os.getcwd()
MODEL_PARAMS_PATH = os.path.join(CWD, "net_params", "cnn", "net_cnn_epoch40.params")
SENTENCE_DIMENSION = 100
POS_DIMENSION = 5
DIMENSION = SENTENCE_DIMENSION + 2 * POS_DIMENSION
FIXED_WORD_LENGTH = 60

input_test = np.load('data_test.npy')

x_test = input_test[:, 3:].reshape((input_test.shape[0], FIXED_WORD_LENGTH, DIMENSION))
x_test = np.expand_dims(x_test, axis=1)
y_test = input_test[:, 0]
print(x_test.shape)
print(y_test.shape)

x_all = x_test
y_all = y_test
x_all = x_all.astype(np.float32)
y_all = y_all.astype(np.int)
print(x_all.shape, y_all.shape)

net = nn.Sequential()
with net.name_scope():
    # net.add(nn.Conv2D(256, kernel_size=(5, DIMENSION), padding=(1, 0), activation='relu'))
    net.add(nn.Conv2D(256, kernel_size=(3, DIMENSION), padding=(1, 0), activation='relu'))
    # net.add(nn.MaxPool2D(pool_size=(FIXED_WORD_LENGTH - 2, 1)))
    net.add(nn.MaxPool2D(pool_size=(FIXED_WORD_LENGTH, 1)))
    net.add(nn.Dense(256, activation='relu'))
    net.add(nn.Dropout(0.5))
    net.add(nn.Dense(11))

net.load_params(MODEL_PARAMS_PATH)
print(net)

label_list = y_all.tolist()
y_hat = net(nd.array(x_all))
predict_list = y_hat.argmax(axis=1).asnumpy().astype(np.int).tolist()
print(precision_recall_fscore_support(label_list, predict_list, average='weighted'))
print(classification_report(label_list, predict_list))