为什么我得到的是数组而不是向量大小？

Question

我想获得矢量大小（46）。但我得到了数组。我使用的数据集是路透社。

我打印 NN 预测的地方是代码的最后几行。

代码：

from keras.datasets import reuters

from keras import models, layers, losses

from keras.utils.np_utils import to_categorical

import numpy as np

(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)

word_index = reuters.get_word_index()

reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

decoded_newswire = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])

def vectorize_sequences(sequences, dimension=10000):

    results = np.zeros((len(sequences), dimension))

    for i, equences in enumerate(sequences):

        results[i, sequences] = 1.

    return results

x_train = vectorize_sequences(train_data)

x_test = vectorize_sequences(test_data)

one_hot_train_labels = to_categorical(train_labels)

one_hot_test_labels = to_categorical(test_labels)

model = models.Sequential()

model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))

model.add(layers.Dense(64, activation='relu'))

model.add(layers.Dense(46, activation='softmax'))

model.compile(optimizer='adam',

            loss='categorical_crossentropy', 

            metrics=['accuracy'])

x_val = x_train[:1000]

partial_x_train = x_train[1000:]

y_val = one_hot_train_labels[:1000]

partial_y_train = one_hot_train_labels[1000:]

history = model.fit(partial_x_train,

                    partial_y_train,

                    epochs=9, 

                    batch_size=128, 

                    validation_data=(x_val, y_val))

predictions = model.predict(x_test)

predictions[0].shape

print(predictions)

输出：

# WHY?                

[[4.2501447e-06 1.9825067e-07 2.3206076e-07 ... 2.1613120e-07

  9.8317461e-09 1.3596014e-07]

 [1.6055314e-02 1.4951903e-01 1.4057434e-04 ... 1.1199807e-04

  1.8230558e-06 2.4111385e-03]

 [7.8554759e-03 6.6994888e-01 1.6705523e-03 ... 4.0704478e-04

  2.4865860e-05 7.2334736e-04]

 ...

Answer 1

用于#%%创建单元格，您将运行独立的代码片段。它看起来像Python Shell