Tflearn Input shape error

Question

Error

"Cannot feed value of shape (128, 1) for Tensor 'TargetsData/Y:0', which has shape '(?,)'".

Code

I have 4 classes and vocab consists of words 17355.

tf.reset_default_graph()
net = tflearn.input_data(shape=(None,trainX.shape[1]),name='input')
net = tflearn.fully_connected(net, 200, activation='ReLU')
net = tflearn.fully_connected(net, 25, activation='ReLU')
net = tflearn.fully_connected(net, 4, activation='softmax')
net = tflearn.regression(net, optimizer='sgd', 
                         learning_rate=0.1, 
                         to_one_hot = True,n_classes =4,
                         loss='categorical_crossentropy')

model = tflearn.DNN(net)
model.fit(trainX, trainY, validation_set=0.1, show_metric=True, batch_size=128, n_epoch=100)

trainX.shape = 12384,17355 , trainY.shape = 12384,1 , testX.shape = 1376,17355 , testY.shape = 1376,1

Answer 1

What causes this error?

The error ”Cannot feed value of shape … for Tensor 'TargetsData/Y:0', which has shape …” is mainly caused by that the shape of trainY is different with the placeholder shape of the estimator (regression) layer.

Why?

In your case, the main problem is that the shape of trainY is (?, 1) which is a 2D tensor, but the shape of the placeholder is (?,) which is an 1D tensor. So we get this error.

How to solve it?

Reshape the trainY to 1D tensor. For you had set the to_one_hot = True in the regression layer, so the placeholder shape is an 1D tensor which contains the class indices. For details you can check the source code:tflearn/tflearn/layers/estimator.py about regression:

   with tf.name_scope(pscope):
        p_shape = [None] if to_one_hot else input_shape
        placeholder = tf.placeholder(shape=p_shape, dtype=dtype, name="Y") 

So, we need to reshape the trainY from (12384,1) to (12384,) before fed to the model.