def test_accuracy(sess, summ_op, acc_val, model, data_handler, config):
svm_train_size = 100
x_syn_data, label_syn_data = None, []
z_pl = tf.placeholder(tf.float32, shape=[None, config.z_dim])
c_pl = tf.placeholder(tf.float32, shape=[None, config.attr_dim])
G_samples = model.G(z_pl, c_pl, reuse=True, is_training=False)
z = sample_z(svm_train_size, config.z_dim)
for idx, ci_attr in enumerate(data_handler.test_attr):
xi_syn_data = sess.run([G_samples],
feed_dict={
z_pl: z,
c_pl: np.tile(ci_attr, (svm_train_size, 1))
})
xi_syn_data = np.squeeze(xi_syn_data, axis=0).reshape(1, -1)
if x_syn_data is None:
x_syn_data = xi_syn_data
else:
x_syn_data = np.vstack((x_syn_data, xi_syn_data))
label_syn_data.extend(data_handler.test_label[idx] *
np.ones(svm_train_size))
svm_model = LinearSVM(config)
svm_model.train(x_syn_data, label_syn_data)
accuracy = svm_model.measure_accuracy(data_handler.test_data,
data_handler.test_label)
summ = sess.run(summ_op, feed_dict={acc_val: accuracy})
return summ
x_val = np.resize(
x_val, (num_val, x_val.shape[1] * x_val.shape[2] * x_val.shape[3]))
x_test = np.resize(
x_test,
(num_test, x_test.shape[1] * x_test.shape[2] * x_test.shape[3]))
# 堆叠数组
x_train = np.hstack([x_train, np.ones((x_train.shape[0], 1))])
x_val = np.hstack([x_val, np.ones((x_val.shape[0], 1))])
x_test = np.hstack([x_test, np.ones((x_test.shape[0], 1))])
svm = LinearSVM()
loss_history = svm.train(x_train,
y_train,
learning_rate=1e-7,
reg=2.5e4,
num_iters=2000,
batch_size=200,
print_flag=True)
y_train_pred = svm.predict(x_train)
num_correct = np.sum(y_train_pred == y_train)
accuracy = np.mean(y_train_pred == y_train)
print('Training correct %d/%d: The accuracy is %f' %
(num_correct.real, x_train.shape[0], accuracy.real))
y_test_pred = svm.predict(x_test)
num_correct = np.sum(y_test_pred == y_test)
accuracy = np.mean(y_test_pred == y_test)
print('Test correct %d/%d: The accuracy is %f' %
(num_correct.real, x_test.shape[0], accuracy.real))
x_test = np.hstack([x_test, np.ones((x_test.shape[0], 1))])
learning_rates = [1.4e-7, 1.5e-7, 1.6e-7]
regularization_strengths = [8000.0, 9000.0, 10000.0, 11000.0, 18000.0, 19000.0, 20000.0, 21000.0]
results = {}
best_lr = None
best_reg = None
best_val = -1 # The highest validation accuracy that we have seen so far.
best_svm = None # The LinearSVM object that achieved the highest validation rate.
for lr in learning_rates:
for reg in regularization_strengths:
svm = LinearSVM()
loss_history = svm.train(x_train, y_train, learning_rate=lr, reg=reg, num_iters=2000)
y_train_pred = svm.predict(x_train)
accuracy_train = np.mean(y_train_pred == y_train)
y_val_pred = svm.predict(x_val)
accuracy_val = np.mean(y_val_pred == y_val)
if accuracy_val > best_val:
best_lr = lr
best_reg = reg
best_val = accuracy_val
best_svm = svm
results[(lr, reg)] = accuracy_train, accuracy_val
print('lr: %e reg: %e train accuracy: %f val accuracy: %f' %
(lr, reg, results[(lr, reg)][0].real, results[(lr, reg)][1].real))
print('Best validation accuracy during cross-validation:\nlr = %e, reg = %e, best_val = %f' %
(best_lr, best_reg, best_val))
