My code is written as this.
def __init__(self, X):
ops.reset_default_graph()
tl.layers.clear_layers_name()
self.sess = tf.Session()
self.input_x = tf.placeholder(tf.float32, shape=[None, 784],name="input")
input_layer = tl.layers.InputLayer(self.input_x)
drop1 = tl.layers.DropoutLayer(input_layer, keep=0.8, name="drop1")
relu1 = tl.layers.DenseLayer(drop1, n_units=800, act = tf.nn.relu)
drop2 = tl.layers.DropoutLayer(relu1, keep=0.5, name="drop2")
self.output = drop2.all_layers[-1]
self.gradient = tf.gradients(self.output,self.input_x)
init_op = tf.initialize_all_variables()
self.sess.run(init_op)
self.output.eval(session=self.sess, feed_dict={self.input_x:X})
As you can see, there is only one placeholder initiated, however, I met
InvalidArgumentError: You must feed a value for placeholder tensor
'Placeholder' with dtype float [[Node: Placeholder =
Placeholderdtype=DT_FLOAT, shape=[],
_device="/job:localhost/replica:0/task:0/cpu:0"]]
I am one hundred percent sure that the X I fed in has type float32, and the shape [1000,784].
Copyright Notice：Content Author:「xxx222」，Reproduced under the CC 4.0 BY-SA copyright license with a link to the original source and this disclaimer.
Link to original article：https://stackoverflow.com/questions/40457938/why-this-code-in-tensorflow-doesnt-work