I am new to tensorflow and I am trying to update some code for bidirectional LSTM from the old version of tensorflow to the newest (1.0), but I get this error:
The form must be of rank 2, but it is of rank 3 for "MatMul_3" (op: "MatMul") with input forms: [100,?, 400], [400,2].
Error on pred_mod.
_weights = { # Hidden layer weights => 2*n_hidden because of foward + backward cells 'w_emb' : tf.Variable(0.2 * tf.random_uniform([max_features,FLAGS.embedding_dim], minval=-1.0, maxval=1.0, dtype=tf.float32),name='w_emb',trainable=False), 'c_emb' : tf.Variable(0.2 * tf.random_uniform([3,FLAGS.embedding_dim],minval=-1.0, maxval=1.0, dtype=tf.float32),name='c_emb',trainable=True), 't_emb' : tf.Variable(0.2 * tf.random_uniform([tag_voc_size,FLAGS.embedding_dim], minval=-1.0, maxval=1.0, dtype=tf.float32),name='t_emb',trainable=False), 'hidden_w': tf.Variable(tf.random_normal([FLAGS.embedding_dim, 2*FLAGS.num_hidden])), 'hidden_c': tf.Variable(tf.random_normal([FLAGS.embedding_dim, 2*FLAGS.num_hidden])), 'hidden_t': tf.Variable(tf.random_normal([FLAGS.embedding_dim, 2*FLAGS.num_hidden])), 'out_w': tf.Variable(tf.random_normal([2*FLAGS.num_hidden, FLAGS.num_classes]))} _biases = { 'hidden_b': tf.Variable(tf.random_normal([2*FLAGS.num_hidden])), 'out_b': tf.Variable(tf.random_normal([FLAGS.num_classes]))} #~ input PlaceHolders seq_len = tf.placeholder(tf.int64,name="input_lr") _W = tf.placeholder(tf.int32,name="input_w") _C = tf.placeholder(tf.int32,name="input_c") _T = tf.placeholder(tf.int32,name="input_t") mask = tf.placeholder("float",name="input_mask") # Tensorflow LSTM cell requires 2x n_hidden length (state & cell) istate_fw = tf.placeholder("float", shape=[None, 2*FLAGS.num_hidden]) istate_bw = tf.placeholder("float", shape=[None, 2*FLAGS.num_hidden]) _Y = tf.placeholder("float", [None, FLAGS.num_classes]) #~ transfortm into Embeddings emb_x = tf.nn.embedding_lookup(_weights['w_emb'],_W) emb_c = tf.nn.embedding_lookup(_weights['c_emb'],_C) emb_t = tf.nn.embedding_lookup(_weights['t_emb'],_T) _X = tf.matmul(emb_x, _weights['hidden_w']) + tf.matmul(emb_c, _weights['hidden_c']) + tf.matmul(emb_t, _weights['hidden_t']) + _biases['hidden_b'] inputs = tf.split(_X, FLAGS.max_sent_length, axis=0, num=None, name='split') lstmcell = tf.contrib.rnn.BasicLSTMCell(FLAGS.num_hidden, forget_bias=1.0, state_is_tuple=False) bilstm = tf.contrib.rnn.static_bidirectional_rnn(lstmcell, lstmcell, inputs, sequence_length=seq_len, initial_state_fw=istate_fw, initial_state_bw=istate_bw) pred_mod = [tf.matmul(item, _weights['out_w']) + _biases['out_b'] for item in bilstm]
Any help was appreciated.