我是tensorflow的新手,尝试在Twitter上运行一个CNN嵌入矩阵(每个嵌入矩阵是574x300字x嵌入长度),每次100条tweet。我一直在下面一行得到错误ValueError: setting an array element with a sequence.
:sess.run(training_op, feed_dict={input_tweets: x_batch, tweet_labels: y_batch})
。你知道吗
filter_size = 2
embedding_size = 300
length_embedding = 575
num_filters = 100
filter_shape = [filter_size, embedding_size, 1, num_filters]
batch_size = 100
n_epochs = 10
n_inputs = length_embedding*embedding_size
n_outputs = 2 #classify between 2 categories
num_train_examples = 2000
with tf.name_scope("inputs"):
input_tweets = tf.placeholder(tf.float32, shape = [batch_size, length_embedding], name="input_tweets")
input_tweets_reshaped = tf.expand_dims(input_tweets, -1)
tweet_labels = tf.placeholder(tf.int32, shape = [batch_size], name="tweet_labels")
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
conv = tf.nn.conv2d(input_tweets_reshaped, W,
strides = [1,1,1,1], padding="VALID", name="conv")
conv_bias = tf.nn.bias_add(conv, b)
#pooling
sequence_length=input_tweets_reshaped.shape[1]
with tf.name_scope("pool"):
pool = tf.nn.max_pool(conv, ksize=[1, sequence_length - filter_size + 1, 1, 1],
strides=[1,1,1,1],
padding="VALID",
name="pool")
pool_flat = tf.reshape(pool, shape=[-1, num_filters])
#fully-connected layer
with tf.name_scope("fc_layer"):
fc_layer = tf.layers.dense(pool_flat, num_filters, activation=tf.nn.relu, name="fc_layer")
#output
with tf.name_scope("output_layer"):
logits = tf.layers.dense(fc_layer, n_outputs, name="output_layer")
Y_proba = tf.nn.softmax(logits, name="Y_proba")
#train
with tf.name_scope("train"):
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=tweet_labels)
loss=tf.reduce_mean(xentropy)
optimizer=tf.train.AdamOptimizer()
training_op=optimizer.minimize(loss)
with tf.name_scope("eval"):
correct = tf.nn.in_top_k(logits, tweet_labels, 1)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
with tf.name_scope("init_and_save"):
init = tf.global_variables_initializer()
saver = tf.train.Saver()
#--run model
with tf.Session() as sess:
init.run()
for epoch in range(n_epochs):
for iteration in range(num_train_examples // batch_size):
print("iteration: "+str(iteration))
x_batch = x_train[iteration*batch_size : (iteration+1)*batch_size]
y_batch = y_train[iteration*batch_size : (iteration+1)*batch_size]
sess.run(training_op, feed_dict={input_tweets: x_batch, tweet_labels: y_batch})
acc_train = accuracy.eval(feed_dict={input_tweets: x_batch, tweet_labels: y_batch})
acc_test = accuracy.eval(feed_dict={input_tweets: x_test, tweet_labels: y_test})
print(epoch, "Train accuracy:", acc_train, "Test accuracy:", acc_test)
x\u batch是一个长度为100的numpy数组,每个元素都是一个维度为575x300的矩阵(尽管当我调用x时_批处理形状,返回(100575)。y\u batch是1和0的1d numpy数组;y_批处理形状返回(100,)。我认为问题可能出在输入的维度上——有人能清楚地看到不匹配是什么吗?你知道吗
谢谢你!你知道吗
conv2d
的输入必须有rank=4
,但您有rank=3
。你知道吗embedding_size
决定过滤器的第二维度,必须小于或等于输入张量的第三维度。第三维度等于1
-展开维度。因此,它不能大于1
!你知道吗tf.layers.conv2d()
自动创建卷积变量。你知道吗tf.layers.conv1d()
它需要一个rank=3
张量作为输入。你知道吗我不确定您想用代码实现什么,但下面是一个有效的修改版本:
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