Neural network with a CSV file in TensorFlow

Question

I am using two tutorials to figure out how to take a CVS file of format:

feature1,feature2....feature20,label
feature1,feature2....feature20,label
...

and train a neural network on it. What I do in the code below is read in the CVS file and group 100 lines at a time into batches: x_batch and y_batch. Next, i try to have the NN learn in batches. However, I get the following error:

"ValueError: Cannot feed value of shape (99,) for Tensor 'Placeholder_1:0', which has shape '(?, 4)'"

I am wondering what i am doing wrong and what another approach might be.

import tensorflow as tf

filename_queue = tf.train.string_input_producer(["VOL_TRAIN.csv"])


line_reader = tf.TextLineReader(skip_header_lines=1)
_, csv_row = line_reader.read(filename_queue)


# Type information and column names based on the decoded CSV.
[[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[""]]

record_defaults = [[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0]]
in1,in2,in3,in4,in5,in6,in7,in8,in9,in10,in11,in12,in13,in14,in15,in16,in17,in18,in19,in20,out = \
    tf.decode_csv(csv_row, record_defaults=record_defaults)

# Turn the features back into a tensor.
features = tf.pack([in1,in2,in3,in4,in5,in6,in7,in8,in9,in10,in11,in12,in13,in14,in15,in16,in17,in18,in19,in20])


# Parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
display_step = 1
num_examples= 33500

# Network Parameters
n_hidden_1 = 256 # 1st layer number of features
n_hidden_2 = 256 # 2nd layer number of features
n_input = 20 # MNIST data input (img shape: 28*28)
n_classes = 4 # MNIST total classes (0-9 digits)

# tf Graph input
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])


# Create model
def multilayer_perceptron(x, weights, biases):
    # Hidden layer with RELU activation
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    # Hidden layer with RELU activation
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.relu(layer_2)
    # Output layer with linear activation
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
    return out_layer


# Store layers weight & bias
weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# Construct model
pred = multilayer_perceptron(x, weights, biases)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Initializing the variables
init = tf.global_variables_initializer()



with tf.Session() as sess:
    #tf.initialize_all_variables().run()
    sess.run(init)
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(num_examples/batch_size)
        # Loop over all batches

        for i in range(total_batch):
            batch_x = []
            batch_y = []
            for iteration in range(1, batch_size):
                example, label = sess.run([features, out])
                batch_x.append(example)
                batch_y.append(label)

            # Run optimization op (backprop) and cost op (to get loss value)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                          y: batch_y})
            # Compute average loss
            avg_cost += c / total_batch
        # Display logs per epoch step
        if epoch % display_step == 0:
            print ("Epoch:", '%04d' % (epoch+1), "cost=", \
                "{:.9f}".format(avg_cost))
    print ("Optimization Finished!")
    coord.request_stop()
    coord.join(threads)

batch_y appears to be (99,) but you're expecting something with n_class. — drpng
– drpng, Commented Feb 14, 2017 at 7:17
As a side note: according to batch_size = 100 you want batches of size 100, but you are actually using batches of size 99. — kafman
– kafman, Commented Feb 14, 2017 at 9:57

rmeertens · Accepted Answer · 2017-02-14 08:48:48Z

1

Your placeholder y specifies you input an array of unknown length, with arrays of length "n_classes" (which is 4). In your feed_dict you give the array batch_y, which is an array of length 99 (your batch_size) with numbers.

What you want to do is change your batch_y variable to have one-hot vectors as input. Please let me know if this works!

answered Feb 14, 2017 at 8:48

rmeertens

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