Data Structure Discrepancy in Tensorflow/TFLearn

Question

I have two datasets, which is like:

input:
array([[[ 0.99309823],
           ...
        [ 0.        ]]])

shape : (1, 2501)

output:
array([[0, 0, 0, ..., 0, 0, 1],
       ..., 
       [0, 0, 0, ..., 0, 0, 0]])
shape : (2501, 9)

And I processed it with TFLearn; as

input_layer = tflearn.input_data(shape=[None,2501])
hidden1 = tflearn.fully_connected(input_layer,1205,activation='ReLU', regularizer='L2', weight_decay=0.001)
dropout1 = tflearn.dropout(hidden1,0.8)

hidden2 = tflearn.fully_connected(dropout1,1205,activation='ReLU', regularizer='L2', weight_decay=0.001)
dropout2 = tflearn.dropout(hidden2,0.8)
softmax = tflearn.fully_connected(dropout2,9,activation='softmax')

# Regression with SGD
sgd = tflearn.SGD(learning_rate=0.1,lr_decay=0.96, decay_step=1000)
top_k=tflearn.metrics.Top_k(3)
net = tflearn.regression(softmax,optimizer=sgd,metric=top_k,loss='categorical_crossentropy')

model = tflearn.DNN(net)
model.fit(input,output,n_epoch=10,show_metric=True, run_id='dense_model')

It works but not the way that I want. It's a DNN model. I want that when I enter 0.95, model must give me corresponding prediction for example [0,0,0,0,0,0,0,0,1]. However, when I want to enter 0.95, it says that,

ValueError: Cannot feed value of shape (1,) for Tensor 'InputData/X:0', which has shape '(?, 2501)'

When I tried to understand I realise that I need (1,2501) shaped data to predict for my wrong based model.

What i want is for every element in input, predict corresponding element in output. As you can see, in the instance dataset,

for [0.99309823], corresponding output is [0,0,0,0,0,0,0,0,1]. I want tflearn to train itself like this.

I may have wrong structured data, or model(probably dataset), I explained all the things, I need help I'm really out of my mind.

Answer 1

Also my friend warned me about same thing as rcmalli. He says reshape:

input = tf.reshape(input, (2501,1)) 

change

input_layer = tflearn.input_data(shape=[None,2501])

to

input_layer = tflearn.input_data(shape=[None, 1]) 

Variable dimension must be "None". In your wrong case, 2501 is the magnitude(or something else, I translated from another lang., but you got it) of your dataset. 1 is constant input magnitude.

Answer 2

Your input data should be Nx1 (N = number of samples) dimensional to archive this transformation ([0.99309823] --> [0,0,0,0,0,0,0,0,1] ). According to your input data shape, it looks more likely including 1 sample with 2501 dimensions.

• ValueError: Cannot feed value of shape (1,) for Tensor 'InputData/X:0', which has shape '(?, 2501)' This error means that tensorflow expecting you to provide a vector with shape (,2501), but you are feeding the network with a vector with shape (1,).

• Example modified code with dummy data:

import numpy as np
import tflearn

#creating dummy data
input_data = np.random.rand(1, 2501)
input_data = np.transpose(input_data) # now shape is (2501,1)
output_data = np.random.randint(8, size=2501)
n_values = 9
output_data = np.eye(n_values)[output_data]

# checking the shapes
print input_data.shape #(2501,1)
print output_data.shape #(2501,9)

input_layer = tflearn.input_data(shape=[None,1]) # now network is expecting ( Nx1 )
hidden1 = tflearn.fully_connected(input_layer,1205,activation='ReLU', regularizer='L2', weight_decay=0.001)
dropout1 = tflearn.dropout(hidden1,0.8)

hidden2 = tflearn.fully_connected(dropout1,1205,activation='ReLU', regularizer='L2', weight_decay=0.001)
dropout2 = tflearn.dropout(hidden2,0.8)
softmax = tflearn.fully_connected(dropout2,9,activation='softmax')

# Regression with SGD
sgd = tflearn.SGD(learning_rate=0.1,lr_decay=0.96, decay_step=1000)
top_k=tflearn.metrics.Top_k(3)
net = tflearn.regression(softmax,optimizer=sgd,metric=top_k,loss='categorical_crossentropy')
model = tflearn.DNN(net)
model.fit(input_data, output_data, n_epoch=10,show_metric=True, run_id='dense_model')