Can't run prediciton because of troubles with tf.placeholder
Apologies, I am new in Tensorflow. I am developing a simple onelayer_perceptron script that just obtaining init parameters trains a Neural Network using Tensorflow:
My compiler complains:
You must feed a value for placeholder tensor 'input' with dtype float
the error occurs here:
input_tensor = tf.placeholder(tf.float32,[None, n_input],name="input")
Plese see what I have done so far:
1) I init my input values
n_input = 10 # Number of input neurons
n_hidden_1 = 10 # Number of hidden layers
n_classes = 3 # Out layers
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
'out': tf.Variable(tf.random_normal([n_hidden_1, n_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
2) Initializing placeholders:
input_tensor = tf.placeholder(tf.float32, [None, n_input], name="input")
output_tensor = tf.placeholder(tf.float32, [None, n_classes], name="output")
3) Train the NN
# Construct model
prediction = onelayer_perceptron(input_tensor, weights, biases)
init = tf.global_variables_initializer()
4) This is my onelayer_perceptron function that just does typical NN calculation matmul layers and weights, add biases and activates using sigmoid
def onelayer_perceptron(input_tensor, weights, biases):
layer_1_multiplication = tf.matmul(input_tensor, weights['h1'])
layer_1_addition = tf.add(layer_1_multiplication, biases['b1'])
layer_1_activation = tf.nn.sigmoid(layer_1_addition)
out_layer_multiplication = tf.matmul(layer_1_activation, weights['out'])
out_layer_addition = out_layer_multiplication + biases['out']
return out_layer_addition
5) Running my script
with tf.Session() as sess:
sess.run(init)
i = sess.run(input_tensor)
print(i)
You are not feeding the input to the place holder; you do it using a feed_dict .
You should do something similar:
out = session.run(Tensor(s)_you_want_to_evaluate, feed_dict={input_tensor: input of size [batch_size,n_input], output_tensor: output of size [batch size, classes] })
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