Question

我已经使用Keras创建了模型。

这是相关的代码。 -https://github.com/CVxTz/ECG_Heartbeat_Classification/blob/master/code/baseline_mitbih.py

我可以运行它并获得模型准确性。

IT可以按预期运行火车和测试数据。

现在，我要测试不带样本记录并获得预测结果。我该怎么做？

我的代码-

df_train = pd.read_csv("mitbih_train.csv", header=None)
df_train = df_train.sample(frac=1)
df_test = pd.read_csv("mitbih_test.csv", header=None)

Y = np.array(df_train[187].values).astype(np.int8)
X = np.array(df_train[list(range(187))].values)[..., np.newaxis]

Y_test = np.array(df_test[187].values).astype(np.int8)
X_test = np.array(df_test[list(range(187))].values)[..., np.newaxis]


def get_model():
    nclass = 5
    inp = Input(shape=(187, 1))
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp)
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = Dropout(rate=0.1)(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = Dropout(rate=0.1)(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = Dropout(rate=0.1)(img_1)
    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = GlobalMaxPool1D()(img_1)
    img_1 = Dropout(rate=0.2)(img_1)

    dense_1 = Dense(64, activation=activations.relu, name="dense_1")(img_1)
    dense_1 = Dense(64, activation=activations.relu, name="dense_2")(dense_1)
    dense_1 = Dense(nclass, activation=activations.softmax, name="dense_3_mitbih")(dense_1)

    model = models.Model(inputs=inp, outputs=dense_1)
    opt = optimizers.Adam(0.001)

    model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc'])
    model.summary()
    return model

model = get_model()
file_path = "baseline_cnn_mitbih.h5"
model.load_weights(file_path)
pred_test = model.predict(X_test)
pred_test = np.argmax(pred_test, axis=-1)
f1 = f1_score(Y_test, pred_test, average="macro")
print("Test f1 score : %s "% f1)
acc = accuracy_score(Y_test, pred_test)
print("Test accuracy score : %s "% acc)

Answer 1

您可以传递188列的单个数组来预测输出。

model.predict(np.array([0,1,..,187]))

使用keras模型预测单个记录的结果

1 个答案: