为什么我得到的是数组而不是向量大小? [英] Why am I getting array instead of vector size?

问题描述

我想获得一个矢量大小(46).但我得到阵列.我使用的数据集是路透社.

I want to get a vector size(46). But I getting array. The dataset that I used is Reuters.

我打印 NN 预测的地方是最后几行代码.

The place where I print NN predictions is the last lines of code.

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, sequences 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]
 ...
 [2.9577111e-06 9.5703072e-06 3.2641565e-05 ... 2.3492355e-06
  1.8574113e-06 3.1159422e-07]
 [1.7232201e-03 1.7063649e-01 1.5664790e-02 ... 4.8910693e-04
  4.2799808e-04 1.0207186e-03]
 [1.7965600e-04 6.5334785e-01 7.2387634e-03 ... 9.2276223e-06
  1.9617393e-05 1.7480283e-05]]

推荐答案

好吧，我得到了我需要的结果.我在 Stack Overflow 的另一个问题中找到了他:如何在 Visual Studio Code 本身中显示图形?

Well, I got result that was need for me. I found him in another question from Stack Overflow: How to show graph in Visual Studio Code itself?

使用 #%% 来创建单元格，您将运行独立的代码片段.它看起来像 Python Shell(这是我喜欢的 Jupyter Notebook).

Use #%% for creating cells in that you will run independent fragments of code. It seems like Python Shell(this is Jupyter Notebook that I love).

我想要的结果

代码

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