如何使用 Keras 预测单个样本答案

【问题标题】：How to predict a single sample with Keras如何使用 Keras 预测单个样本
【发布时间】：2021-07-07 02:57:15
【问题描述】：

我正在尝试实现一个全卷积神经网络，并且可以在训练后成功地测试模型在测试集上的准确性。但是，我想使用该模型仅对单个样本进行预测。培训是分批进行的。我相信我所缺少的与批量大小和输入形状有关。这是网络的配置：

def read(file_name):
    data = np.loadtxt(file_name, delimiter="\t")
    y = data[:, 0]
    x = data[:, 1:]
    return x, y.astype(int)

train_data, train_labels = read("FordA_TRAIN.tsv")
test_data, test_labels = read("FordA_TEST.tsv")

train_data = train_data.reshape((train_data.shape[0], train_data.shape[1], 1))
test_data = test_data.reshape((test_data.shape[0], test_data.shape[1], 1))

num_classes = len(np.unique(train_labels))

#print(train_data[0])

# Shuffle the data to prepare for validation_split (and prevent overfitting for class order)

idx = np.random.permutation(len(train_data))
train_data = train_data[idx]
train_labels = train_labels[idx]

#Standardize labels to have a value between 0 and 1 rather than -1 and 1.

train_labels[train_labels == -1] = 0
test_labels[test_labels == -1] = 0

def make_model(input_shape):
    input_layer = keras.layers.Input(input_shape)

    conv1 = keras.layers.Conv1D(filters=64, kernel_size=3, padding="same")(input_layer)
    conv1 = keras.layers.BatchNormalization()(conv1)
    conv1 = keras.layers.ReLU()(conv1)

    conv2 = keras.layers.Conv1D(filters=64, kernel_size=3, padding="same")(conv1)
    conv2 = keras.layers.BatchNormalization()(conv2)
    conv2 = keras.layers.ReLU()(conv2)

    conv3 = keras.layers.Conv1D(filters=64, kernel_size=3, padding="same")(conv2)
    conv3 = keras.layers.BatchNormalization()(conv3)
    conv3 = keras.layers.ReLU()(conv3)

    gap = keras.layers.GlobalAveragePooling1D()(conv3)

    output_layer = keras.layers.Dense(num_classes, activation="softmax")(gap)

    return keras.models.Model(inputs=input_layer, outputs=output_layer)


model = make_model(input_shape=train_data.shape[1:])
keras.utils.plot_model(model, show_shapes=True)

epochs = 500
batch_size = 32

callbacks = [
    keras.callbacks.ModelCheckpoint(
        "best_model.h5", save_best_only=True, monitor="val_loss"
    ),
    keras.callbacks.ReduceLROnPlateau(
        monitor="val_loss", factor=0.5, patience=20, min_lr=0.0001
    ),
    keras.callbacks.EarlyStopping(monitor="val_loss", mode = 'min', patience=50, verbose=1),
]
model.compile(
    optimizer="adam",
    loss="sparse_categorical_crossentropy",
    metrics=["sparse_categorical_accuracy"],
)
history = model.fit(
    train_data,
    train_labels,
    batch_size=batch_size,
    epochs=epochs,
    callbacks=callbacks,
    validation_split=0.2,
    verbose=1,
)

model = keras.models.load_model("best_model.h5")

test_loss, test_acc = model.evaluate(test_data, test_labels)

print("Test accuracy", test_acc)
print("Test loss", test_loss)

上面的代码可以成功的显示出准确率收敛的地方。现在，我想对单个样本进行预测。到目前为止，我有：

def read(file_name):
    data = np.loadtxt(file_name, delimiter="\t")
    y = data[:, 0]
    x = data[:, 1:]
    return x, y.astype(int)

test_data, test_labels = read("FordA_TEST_B.tsv")
test_data = test_data.reshape((test_data.shape[0], test_data.shape[1], 1))

test_labels[test_labels == -1] = 0

print(test_data)

model = keras.models.load_model("forda_original_model.h5")

q = model.predict(test_data[0])

这会引发错误：ValueError: Error when checks input: expected input_1 to have 3 dimensions, but got array with shape (500, 1)

输入必须如何重塑，遵循什么规则？非常感谢任何帮助！

【问题讨论】：

我认为您的模型需要批量维度，请尝试 test_data[0][None] 它将在第一个（批量）维度中添加一个新轴。
是的！那是缺少的部分，谢谢！

标签： python numpy tensorflow machine-learning keras

【解决方案1】：

从评论中复制：

模型需要一个批次维度。因此，要预测单个模型，只需通过运行扩展维度以创建单个大小的批次：

q = model.predict(test_data[0][None,...])

或

q = model.predict(test_data[0][np.newaxis,...])

【讨论】：