好的.我知道我们可以使用以下方法限制Keras(TF后端)模型使用的核心数量:
K.set_session(K.tf.Session(config=K.tf.ConfigProto(intra_op_parallelism_threads=2, inter_op_parallelism_threads=2, device_count = {'CPU': 2})))
我们可以像这样指定单个张量运算:
with tf.device('/cpu:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
但是,如果我们想要指定Keras模型使用的各个CPU列表呢？
我正在尝试使用该tfgo包在Go中实现我的Keras神经网络.该模型包括2个常规输入和2个Keras嵌入层.它看起来像这样:
embedding_layer = Embedding(vocab_size,
100,
weights=[embedding_matrix],
input_length=100,
trainable=False)
sequence_input = Input(shape=(max_length,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
text_lstm = Bidirectional(LSTM(256))(embedded_sequences)
text_lstm = Dropout(0.5)(text_lstm)
text_lstm = Dense(512, activation='relu')(text_lstm )
text_lstm = Dropout(0.5)(text_lstm)
text_lstm = Dense(256, activation='relu')(text_lstm)
text_lstm = Dropout(0.5)(text_lstm)
text_lstm = Dense(128, activation='relu')(text_lstm)
text_lstm = Dropout(0.5)(text_lstm)
title_input = Input(shape=(max_title_length,), dtype='int32')
title_embed = Embedding(vocab_size, embedding_vector_length, input_length=max_title_length)(title_input)
title_lstm = Bidirectional(LSTM(128))(title_embed)
title_lstm = Dropout(0.5)(title_lstm)
title_lstm = Dense(512, activation='relu')(title_lstm )
title_lstm = Dropout(0.5)(title_lstm)
title_lstm = Dense(256, activation='relu')(title_lstm)
title_lstm = Dropout(0.5)(title_lstm)
title_lstm = Dense(128, activation='relu')(title_lstm)
title_lstm …我浏览了 web/docs，但找不到为什么这不起作用。
我正在尝试从 JSON 文件中插入数据（引号）：{
"quotesArray":[{
"personName": "Albert Einstein",
"quote": "Look deep into nature, and then you will understand everything better."
},
{
"personName": "Stephen Hawking",
"quote": "Look deep into nature, and then you will understand everything better."
},
{
"personName": "Confucius",
"quote": "Life is really simple, but we insist on making it complicated."
}]}
使用以下 JS 进入一个简单的 HTML div：
$(document).ready(function() {
$("#realquote").on("click", function() {
$.getJSON('/json/quotes.json', function(data) {
var html = " ";
data.forEach(function(val) {
var keys = Object.keys(val);
html …