Tensorflow Typeerror: Fetch Argument None Has Invalid Type

I was doing cs231n assignment 2 and encountered this problem. I'm using tensorflow-gpu 1.5.0 Code as following # define our input (e.g. the data that changes every batch) # The fir

Solution 1:

The problem is that the y_out argument to sess.run() is None, whereas it must be a tf.Tensor (or tensor-like object, such as a tf.Variable) or a tf.Operation.

In your example, y_out is defined by the following code:

# define modeldef complex_model(X,y,is_training):
    pass

y_out =complex_model(X,y,is_training)

complex_model() doesn't return a value, so y_out = complex_model(...) will set y_out to None. I'm not sure if this function is representative of your real code, but it's possible that your real complex_model() function is also missing a return statement.

Solution 2:

I believe that mrry is right.

If you give a second look the the notebook Assignment 2 - Tensorflow.ipynb, you will notice the description cell as follows :

Training a specific model

In this section, we're going to specify a model for you to construct. The goal here isn't to get good performance (that'll be next), but instead to get comfortable with understanding the TensorFlow documentation and configuring your own model.

Using the code provided above as guidance, and using the following TensorFlow documentation, specify a model with the following architecture:

7x7 Convolutional Layer with32 filters and stride of 1
ReLU Activation Layer
Spatial Batch Normalization Layer (trainable parameters, with scale and centering)
2x2 Max Pooling layer with a stride of 2
Affine layer with1024 output units
ReLU Activation Layer
Affine layer from1024input units to 10 outputs

Which is asking you to define a model inside the function

# define modeldefcomplex_model(X,y,is_training):
    pass

Just like they did in

def simple_model(X,y):
    # define our weights (e.g. init_two_layer_convnet)

    # setup variables
    Wconv1 = tf.get_variable("Wconv1", shape=[7, 7, 3, 32])
    bconv1 = tf.get_variable("bconv1", shape=[32])
    W1 = tf.get_variable("W1", shape=[5408, 10])
    b1 = tf.get_variable("b1", shape=[10])

    # define our graph (e.g. two_layer_convnet)
    a1 = tf.nn.conv2d(X, Wconv1, strides=[1,2,2,1], padding='VALID') + bconv1
    h1 = tf.nn.relu(a1)
    h1_flat = tf.reshape(h1,[-1,5408])
    y_out = tf.matmul(h1_flat,W1) + b1
    return y_out

Hope this helps!