为什么不能设置HTML元素的“值”属性？

Question

当我尝试运行以下代码时，我不断收到错误消息：

  

无法在“ id”处将null的“ value”属性设置为

我试图将属性从value更改为innerHTML，但这没有用。如果您能帮助我，我将不胜感激。谢谢！

<div class="input-field s6 col" style="color: white; background-color:transparent;">
  <input id="avatar_url" id="ipoza" type="text" onfocus="style='background-color:white;'" onblur="style='background-color:transparent'" value="http://i01.c.aliimg.com/img/ibank/2014/101/288/1614882101_2028072840.jpg"> <label for="avatar_url"><p>Poza de Profil</p></label>
</div>

<button onclick="i()"></button>
<script type="text/javascript">
  function i() {
    document.getElementById("ipoza").value = "Link";
  }
</script>

Answer 1

每个元素只能有一个import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os train_path='/Users/David/Deskt...' batch_size_train=100 num_epochs=1 tf.logging.set_verbosity(tf.logging.INFO) sess=tf.Session() #Convolutional Model def cnn_model(features,labels,mode): #Capa de ingreso input_layer=tf.reshape(features["x"],[-1,224,224,3]) #Capa convolucional 1........ conv1=tf.layers.conv2d( inputs=input_layer, filters=64, kernel_size=[10,10], padding="same", activation=tf.nn.relu, name="Convolucion_1") #Pooling 1......... pool1=tf.layers.max_pooling2d(inputs=conv1,pool_size=[2,2],strides=2,name="Pool_1") conv2=tf.layers.conv2d( inputs=pool1, filters=128, kernel_size=[10,10], padding="same", activation=tf.nn.relu, name="Convolucion_2") pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2,name="Pool_2") conv3=tf.layers.conv2d( inputs=pool2, filters=192, kernel_size=[10,10], padding="same", activation=tf.nn.relu, name="Convolucion_3") pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2,name="Pool_3") conv4=tf.layers.conv2d( inputs=pool3, filters=256, kernel_size=[10,10], padding="same", activation=tf.nn.relu, name="Convolucion_4") pool4 = tf.layers.max_pooling2d(inputs=conv4, pool_size=[2, 2], strides=2,name="Pool_4") conv5=tf.layers.conv2d( inputs=pool4, filters=320, kernel_size=[10,10], padding="same", activation=tf.nn.relu, name="Convolucion_5") pool5 = tf.layers.max_pooling2d(inputs=conv5, pool_size=[2, 2], strides=2,name="Pool_5") pool5_flat=tf.reshape(pool5,[-1,7*7*320],name="Flat_Pool") #Deep neural network.............. dense=tf.layers.dense(inputs=pool5_flat,units=10000,activation=tf.nn.relu,name="Capa_1") dense1=tf.layers.dense(inputs=dense,units=7000,activation=tf.nn.relu,name="Capa_2") dense2=tf.layers.dense(inputs=dense1,units=4000,activation=tf.nn.relu,name="Capa_3") dense3=tf.layers.dense(inputs=dense2,units=1000,activation=tf.nn.relu,name="Capa_4") dense4=tf.layers.dense(inputs=dense3,units=500,activation=tf.nn.relu,name="Capa_5") logits=tf.layers.dense(inputs=dense4,units=2,name="Capa_final") onehot_labels = tf.one_hot(indices=labels, depth=2) t=tf.nn.softmax(logits, name="softmax_tensor") loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels, logits=logits) tf.summary.scalar('loss',loss) ds=tf.train.SummarySaverHook(save_steps=1,output_dir="/Users/David/Desktop/David/Tesis/Practica/Programas/CNN/Model_Chekpoint",summary_op=tf.summary.merge_all()) loss_hook = tf.train.LoggingTensorHook(tensors={"loss":loss}, every_n_iter=1) if mode==tf.estimator.ModeKeys.TRAIN: optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.001) train_op=optimizer.minimize( loss=loss, global_step=tf.train.get_global_step()) return tf.estimator.EstimatorSpec(mode=mode,loss=loss,train_op=train_op,training_hooks=[ds,loss_hook]) def read_file(filename_queue): #Funcion para leer el archivo tf.record, y retornamos el next recrod reader=tf.TFRecordReader() _,serialized_example=reader.read(filename_queue) #Se decodifica el tf.record retornando un diccionario feature={'train/image':tf.FixedLenFeature([],tf.string), 'train/label':tf.FixedLenFeature([],tf.int64)} features=tf.parse_single_example(serialized_example,features=feature,name="Decodificacion_Parse") #Convertimos el string a numeros de los decodificados features image=tf.decode_raw(features['train/image'],tf.float32,name="imagenes_decod")* (1 / 255.0) #Convertimos a datos label=tf.cast(features['train/label'],dtype=tf.int32,name="label_decod") #Reshape data image=tf.reshape(image,[224,224,3]) return image,label def input_pipeline(filenames,batch_size): #Creacion de una lista de los archivos filename_queue=tf.train.string_input_producer([filenames],num_epochs=1,shuffle=True,name="Creacion_lista_archiv") images,labels=read_file(filename_queue) #Mezclar (shuffle) los datos de entrada min_after_dequeue=100 capacity=min_after_dequeue+3*batch_size images,labels=tf.train.shuffle_batch([images,labels],batch_size=batch_size,capacity=capacity,num_threads=2,min_after_dequeue=min_after_dequeue,name="Shuffle_data_in") return images,labels def main(unused_argv): #Lectura y Decodificacion de datos img_train,lbl_train=input_pipeline(train_path,batch_size_train) #Estimator - Modelo gun_detector=tf.estimator.Estimator(model_fn=cnn_model,model_dir="/Users/David/Desktop/David/Tesis/Practica/Programas/CNN/Model_Chekpoint") #Inicializacion de variables y run de la session init_op=tf.group(tf.global_variables_initializer(),tf.local_variables_initializer()) sess.run(init_op) #Corremos las filas(queue) que se crearon en el grafico computacional coord = tf.train.Coordinator() threads=tf.train.start_queue_runners(sess=sess,coord=coord) try: while not coord.should_stop(): img,lbl=sess.run([img_train,lbl_train]) train_input_fn = tf.estimator.inputs.numpy_input_fn( x={"x": img}, y=lbl, batch_size=70, num_epochs=None, shuffle=True) gun_detector.train( input_fn=train_input_fn, steps=5000) except tf.errors.OutOfRangeError: print('Done training -- epoch limit reached') finally: coord.request_stop() coord.join(threads) sess.close() if __name__ == '__main__': tf.app.run() 。您的id有2个input，这是不允许的。只需给它id中的单个id，就可以用ipoza进行选择。

document.getElementById("ipoza")