tf.nn.embedding_lookup()
我正在尝试通过 TensorFlow函数“从头开始”学习 imdb 数据集的单词表示。如果我理解正确,我必须在另一个隐藏层之前设置一个嵌入层,然后当我执行梯度下降时,该层将“学习”该层权重中的单词表示。但是,当我尝试这样做时,我的嵌入层和网络的第一个全连接层之间出现了形状错误。
def multilayer_perceptron(_X, _weights, _biases):
with tf.device('/cpu:0'), tf.name_scope("embedding"):
W = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),name="W")
embedding_layer = tf.nn.embedding_lookup(W, _X)
layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(embedding_layer, _weights['h1']), _biases['b1']))
layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2']))
return tf.matmul(layer_2, weights['out']) + biases['out']
x = tf.placeholder(tf.int32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
pred = multilayer_perceptron(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred,y))
train_step = tf.train.GradientDescentOptimizer(0.3).minimize(cost)
init = tf.initialize_all_variables()
我得到的错误是:
ValueError: Shapes TensorShape([Dimension(None), Dimension(300), Dimension(128)])
and TensorShape([Dimension(None), Dimension(None)]) must have the same rank