Tensorflow: TypeError: get_variable() got multiple values for keyword argument 'name' - variables

Question

I am training the "Show and tell" model using tensorflow in which the model automatically generates the captions of the images. How ever I am getting this error.
This is the traceback:
TypeError Traceback (most recent call
last)
<ipython-input-14-b6da0a27b701> in <module>()
1 try:
2 #train(.001,False,False) #train from scratch
----> 3 train(.001,True,True) #continue training from pretrained weights #epoch500
4 #train(.001) #train from previously saved weights
5 except KeyboardInterrupt:
<ipython-input-13-39693d0edd0a> in train(learning_rate, continue_training, transfer)
23 n_words = len(wordtoix)
24 maxlen = np.max( [x for x in map(lambda x: len(x.split(' ')), captions) ] )
---> 25 caption_generator = Caption_Generator(dim_in, dim_hidden, dim_embed, batch_size, maxlen+2, n_words, init_b)
26
27 loss, image, sentence, mask = caption_generator.build_model()
<ipython-input-12-1b31c4175b3a> in __init__(self, dim_in, dim_embed, dim_hidden, batch_size, n_lstm_steps, n_words, init_b)
11 # declare the variables to be used for our word embeddings
12 with tf.device("/cpu:0"):
---> 13 self.word_embedding = tf.get_variable(tf.random_uniform([self.n_words, self.dim_embed], -0.1, 0.1), name='word_embedding')
14
15 self.embedding_bias = tf.get_variable(tf.zeros([dim_embed]), name='embedding_bias')
TypeError: get_variable() got multiple values for keyword argument 'name'
The problem might be that I am passing some extra arguments to the get_variable initializer but I unable to trace it where this problem is occurring.
Here is the code:
def get_data(annotation_path, feature_path):
annotations = pd.read_table(annotation_path, sep='\t', header=None, names=['image', 'caption'])
return np.load(feature_path,'r'), annotations['caption'].values
def preProBuildWordVocab(sentence_iterator, word_count_threshold=30): # function from Andre Karpathy's NeuralTalk
print('preprocessing %d word vocab' % (word_count_threshold, ))
word_counts = {}
nsents = 0
for sent in sentence_iterator:
nsents += 1
for w in sent.lower().split(' '):
word_counts[w] = word_counts.get(w, 0) + 1
vocab = [w for w in word_counts if word_counts[w] >= word_count_threshold]
print('preprocessed words %d -> %d' % (len(word_counts), len(vocab)))
ixtoword = {}
ixtoword[0] = '.'
wordtoix = {}
wordtoix['#START#'] = 0
ix = 1
for w in vocab:
wordtoix[w] = ix
ixtoword[ix] = w
ix += 1
word_counts['.'] = nsents
bias_init_vector = np.array([1.0*word_counts[ixtoword[i]] for i in ixtoword])
bias_init_vector /= np.sum(bias_init_vector)
bias_init_vector = np.log(bias_init_vector)
bias_init_vector -= np.max(bias_init_vector)
return wordtoix, ixtoword, bias_init_vector.astype(np.float32)
class Caption_Generator():
def __init__(self, dim_in, dim_embed, dim_hidden, batch_size, n_lstm_steps, n_words, init_b):
self.dim_in = dim_in
self.dim_embed = dim_embed
self.dim_hidden = dim_hidden
self.batch_size = batch_size
self.n_lstm_steps = n_lstm_steps
self.n_words = n_words
# declare the variables to be used for our word embeddings
with tf.device("/cpu:0"):
self.word_embedding = tf.get_variable(tf.random_uniform([self.n_words, self.dim_embed], -0.1, 0.1), name='word_embedding')
self.embedding_bias = tf.get_variable(tf.zeros([dim_embed]), name='embedding_bias')
# declare the LSTM itself
self.lstm = tf.contrib.rnn.BasicLSTMCell(dim_hidden)
# declare the variables to be used to embed the image feature embedding to the word embedding space
self.img_embedding = tf.get_variable(tf.random_uniform([dim_in, dim_hidden], -0.1, 0.1), name='img_embedding')
self.img_embedding_bias = tf.get_variable(tf.zeros([dim_hidden]), name='img_embedding_bias')
# declare the variables to go from an LSTM output to a word encoding output
self.word_encoding = tf.get_variable(tf.random_uniform([dim_hidden, n_words], -0.1, 0.1), name='word_encoding')
# initialize this bias variable from the preProBuildWordVocab output
self.word_encoding_bias = tf.get_variable(init_b, name='word_encoding_bias')
def build_model(self):
# declaring the placeholders for our extracted image feature vectors, our caption, and our mask
# (describes how long our caption is with an array of 0/1 values of length `maxlen`
img = tf.placeholder(tf.float32, [self.batch_size, self.dim_in])
caption_placeholder = tf.placeholder(tf.int32, [self.batch_size, self.n_lstm_steps])
mask = tf.placeholder(tf.float32, [self.batch_size, self.n_lstm_steps])
# getting an initial LSTM embedding from our image_imbedding
image_embedding = tf.matmul(img, self.img_embedding) + self.img_embedding_bias
# setting initial state of our LSTM
state = self.lstm.zero_state(self.batch_size, dtype=tf.float32)
total_loss = 0.0
with tf.variable_scope("RNN"):
for i in range(self.n_lstm_steps):
if i > 0:
#if this isn’t the first iteration of our LSTM we need to get the word_embedding corresponding
# to the (i-1)th word in our caption
with tf.device("/cpu:0"):
current_embedding = tf.nn.embedding_lookup(self.word_embedding, caption_placeholder[:,i-1]) + self.embedding_bias
else:
#if this is the first iteration of our LSTM we utilize the embedded image as our input
current_embedding = image_embedding
if i > 0:
# allows us to reuse the LSTM tensor variable on each iteration
tf.get_variable_scope().reuse_variables()
out, state = self.lstm(current_embedding, state)
#out, state = self.tf.nn.dynamic_rnn(current_embedding, state)
if i > 0:
#get the one-hot representation of the next word in our caption
labels = tf.expand_dims(caption_placeholder[:, i], 1)
ix_range=tf.range(0, self.batch_size, 1)
ixs = tf.expand_dims(ix_range, 1)
concat = tf.concat([ixs, labels],1)
onehot = tf.sparse_to_dense(
concat, tf.stack([self.batch_size, self.n_words]), 1.0, 0.0)
#perform a softmax classification to generate the next word in the caption
logit = tf.matmul(out, self.word_encoding) + self.word_encoding_bias
xentropy = tf.nn.softmax_cross_entropy_with_logits(logits=logit, labels=onehot)
xentropy = xentropy * mask[:,i]
loss = tf.reduce_sum(xentropy)
total_loss += loss
total_loss = total_loss / tf.reduce_sum(mask[:,1:])
return total_loss, img, caption_placeholder, mask
### Parameters ###
dim_embed = 256
dim_hidden = 256
dim_in = 4096
batch_size = 128
momentum = 0.9
n_epochs = 150
def train(learning_rate=0.001, continue_training=False, transfer=True):
tf.reset_default_graph()
feats, captions = get_data(annotation_path, feature_path)
wordtoix, ixtoword, init_b = preProBuildWordVocab(captions)
np.save('data/ixtoword', ixtoword)
index = (np.arange(len(feats)).astype(int))
np.random.shuffle(index)
sess = tf.InteractiveSession()
n_words = len(wordtoix)
maxlen = np.max( [x for x in map(lambda x: len(x.split(' ')), captions) ] )
caption_generator = Caption_Generator(dim_in, dim_hidden, dim_embed, batch_size, maxlen+2, n_words, init_b)
loss, image, sentence, mask = caption_generator.build_model()
saver = tf.train.Saver(max_to_keep=100)
global_step=tf.Variable(0,trainable=False)
learning_rate = tf.train.exponential_decay(learning_rate, global_step,
int(len(index)/batch_size), 0.95)
train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss)
tf.global_variables_initializer().run()
if continue_training:
if not transfer:
saver.restore(sess,tf.train.latest_checkpoint(model_path))
else:
saver.restore(sess,tf.train.latest_checkpoint(model_path_transfer))
losses=[]
for epoch in range(n_epochs):
for start, end in zip( range(0, len(index), batch_size), range(batch_size, len(index), batch_size)):
current_feats = feats[index[start:end]]
current_captions = captions[index[start:end]]
current_caption_ind = [x for x in map(lambda cap: [wordtoix[word] for word in cap.lower().split(' ')[:-1] if word in wordtoix], current_captions)]
current_caption_matrix = sequence.pad_sequences(current_caption_ind, padding='post', maxlen=maxlen+1)
current_caption_matrix = np.hstack( [np.full( (len(current_caption_matrix),1), 0), current_caption_matrix] )
current_mask_matrix = np.zeros((current_caption_matrix.shape[0], current_caption_matrix.shape[1]))
nonzeros = np.array([x for x in map(lambda x: (x != 0).sum()+2, current_caption_matrix )])
for ind, row in enumerate(current_mask_matrix):
row[:nonzeros[ind]] = 1
_, loss_value = sess.run([train_op, loss], feed_dict={
image: current_feats.astype(np.float32),
sentence : current_caption_matrix.astype(np.int32),
mask : current_mask_matrix.astype(np.float32)
})
print("Current Cost: ", loss_value, "\t Epoch {}/{}".format(epoch, n_epochs), "\t Iter {}/{}".format(start,len(feats)))
print("Saving the model from epoch: ", epoch)
saver.save(sess, os.path.join(model_path, 'model'), global_step=epoch)
try:
#train(.001,False,False) #train from scratch
train(.001,True,True) #continue training from pretrained weights #epoch500
#train(.001) #train from previously saved weights
except KeyboardInterrupt:
print('Exiting Training')

Answer 1

In your constructor, try
self.word_embedding = tf.get_variable("word_embedding", initializer=tf.random_uniform([self.n_words, self.dim_embed], -0.1, 0.1))
The thing is, the first position argument is name and you have the initializer there instead, and then you again define the name, hence the error.
You need to make likewise changes everywhere you use tf.get_variable