MarketVectors Error after import from iPython to Python, error not related...

[wanfuse123]

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ERROR

('self.logits = ', <tf.Tensor 'ff/fully_connected_2/BiasAdd:0' shape=(?, 11) dtype=float32>)
('self.target_data', <tf.Tensor 'Placeholder_1:0' shape=(?,) dtype=int32>)
Traceback (most recent call last):
File "./preparedata-manual-upgraded.py", line 204, in
model = Model()
File "./preparedata-manual-upgraded.py", line 187, in init
self.losses = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.logits,logits=self.target_data)
File "/home/steven/Practical-DataScience/DataScience/local/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.py", line 1686, in sparse_softmax_cross_entropy_with_logits
(labels_static_shape.ndims, logits.get_shape().ndims))
ValueError: Rank mismatch: Rank of labels (received 2) should equal rank of logits minus 1 (received 1)

CODE IN QUESTION

class Model():
def init(self):
global_step = tf.contrib.framework.get_or_create_global_step()
self.input_data = tf.placeholder(dtype=tf.float32,shape=[None,num_features])
self.target_data = tf.placeholder(dtype=tf.int32,shape=[None])
self.dropout_prob = tf.placeholder(dtype=tf.float32,shape=[])
with tf.variable_scope("ff"):
droped_input = tf.nn.dropout(self.input_data,keep_prob=self.dropout_prob)

        layer_1 = tf.contrib.layers.fully_connected(
            num_outputs=hidden_1_size,
            inputs=droped_input,
        )
        layer_2 = tf.contrib.layers.fully_connected(
            num_outputs=hidden_2_size,
            inputs=layer_1,
        )
        self.logits = tf.contrib.layers.fully_connected(
            num_outputs=num_classes,
            activation_fn =None,
            inputs=layer_2,
        )
    with tf.variable_scope("loss"):
        print ("self.logits = ",self.logits) 
        print ("self.target_data", self.target_data)

exit()

        self.losses = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.logits,logits=self.target_data)
        mask = (1-tf.sign(1-self.target_data)) #Don't give credit for flat days
        mask = tf.cast(mask,tf.float32)
        self.loss = tf.reduce_sum(self.losses)
    
    with tf.name_scope("train"):
      opt = tf.train.AdamOptimizer(lr)
      gvs = opt.compute_gradients(self.loss)
      self.train_op = opt.apply_gradients(gvs, global_step=global_step)
    
    with tf.name_scope("predictions"):
        self.probs = tf.nn.softmax(self.logits)
        self.predictions = tf.argmax(self.probs, 1)
        correct_pred = tf.cast(tf.equal(self.predictions, tf.cast(self.target_data,tf.int64)),tf.float64)
        self.accuracy = tf.reduce_mean(correct_pred)

---

PRINTED OUTPUT OF VARIABLES BEFORE ENTERING THE FUNCTION

[[ 2 3 11 6 1 7 7 3 3 4 5]
[ 2 3 8 7 8 7 6 2 2 2 3]
[ 1 4 9 5 2 13 5 11 5 3 2]
[ 1 6 7 8 5 15 6 1 7 4 2]
[ 1 3 6 2 3 9 10 5 7 4 0]
[ 0 5 11 3 3 6 6 4 5 6 2]
[ 1 3 15 3 3 12 12 1 4 2 4]
[ 0 4 8 3 3 8 12 2 10 3 0]
[ 0 2 16 6 3 9 12 2 3 0 1]
[ 0 7 11 5 2 7 10 4 4 4 2]
[ 3 3 10 6 6 9 6 4 1 4 0]]
[[91 37 75]
[76 35 89]
[92 30 75]]

---

RELEVANT TENSOR FLOW DOC

https://www.tensorflow.org/api_docs/python/tf/nn/sparse_softmax_cross_entropy_with_logits