I have been working on this code for a while and it gave me a lot of headaches before I got it to work. It basically tries to use the MNIST dataset to classify handwritten digits. I am not using the prepackaged MNIST in TensorFlow because I want to learn preprocessing the data myself and for deeper understanding of TensorFlow.

It's finally working, but I would love it if someone with expertise could take a look at it and tell me what they think, and if the results it's producing are actually real stats or if it's overfitting.

It's giving me accuracy between 80% and 91%. The dataset I'm using is from here.

import numpy as np
import tensorflow as tf
sess = tf.Session()
from sklearn import preprocessing
import matplotlib.pyplot as plt
with tf.Session() as sess:
    # lets load the file
    train_file = 'mnist_train.csv'
    test_file = 'mnist_test.csv'
    #train_file = 'mnist_train_small.csv'
    #test_file = 'mnist_test_small.csv'

    train = np.loadtxt(train_file, delimiter=',')
    test = np.loadtxt(test_file, delimiter=',')

    x_train = train[:,1:785]
    y_train = train[:,:1]

    x_test = test[:,1:785]
    y_test = test[:,:1]

    # lets normalize the data
    def normalize(input_data):
        minimum = input_data.min(axis=0)
        maximum = input_data.max(axis=0)
        #normalized = (input_data - minimum) / ( maximum - minimum )
        normalized = preprocessing.normalize(input_data, norm='l2')
        return normalized

    # convert to a onehot array 
    def one_hot(input_data):
        one_hot = []
        for item in input_data:
            if item == 0.:
                one_h = [1.,0.,0.,0.,0.,0.,0.,0.,0.,0.]
            elif item == 1.:
                one_h = [0.,1.,0.,0.,0.,0.,0.,0.,0.,0.]
            elif item == 2.:
                one_h = [0.,0.,1.,0.,0.,0.,0.,0.,0.,0.]
            elif item == 3.:
                one_h = [0.,0.,0.,1.,0.,0.,0.,0.,0.,0.]
            elif item == 4.:
                one_h = [0.,0.,0.,0.,1.,0.,0.,0.,0.,0.]
            elif item == 5.:
                one_h = [0.,0.,0.,0.,0.,1.,0.,0.,0.,0.]
            elif item == 6.:
                one_h = [0.,0.,0.,0.,0.,0.,1.,0.,0.,0.]
            elif item == 7.:
                one_h = [0.,0.,0.,0.,0.,0.,0.,1.,0.,0.]
            elif item == 8.:
                one_h = [0.,0.,0.,0.,0.,0.,0.,0.,1.,0.]
            elif item == 9.:
                one_h = [0.,0.,0.,0.,0.,0.,0.,0.,0.,1.]

        one_hot = np.array(one_hot)
        #one_hot = one_hot.reshape(len(one_hot),10,1)
        #one_hot = one_hot.reshape(len(one_hot), 7,1)
        #return tf.constant([one_hot])
        return one_hot
    def one_hot_tf(val):
        indices = val
        depth = 10
        on_value = 1.0
        off_value = 0.0
        axis = -1
        oh = tf.one_hot(indices, depth,
                   on_value=on_value, off_value=off_value,
                   axis=axis, dtype=tf.float32,
        return (oh)
    x_train = normalize(x_train)
    x_test =  normalize(x_test)
    #    x_train = sess.run(tf.convert_to_tensor(x_train))
    #    x_test =  sess.run(tf.convert_to_tensor(x_test))

    data_initializer = tf.placeholder(dtype=x_train.dtype,
    label_initializer = tf.placeholder(dtype=x_test.dtype,
    x_train= sess.run(tf.Variable(data_initializer, trainable=False, collections=[]))
    x_test = sess.run(tf.Variable(label_initializer, trainable=False, collections=[]))

    y_test =  one_hot(y_test)
    y_train =  one_hot(y_train)
    #   y_test =  sess.run(one_hot_tf(y_test))
    #   y_train =  sess.run(one_hot_tf(y_train))

    # define the parameters
    input_nodes = 784
    output_nodes = 10
    hl1_nodes = 500
    hl2_nodes = 500
    hl3_nodes = 500
    epochs = 10
    x = tf.placeholder(tf.float32, [None, input_nodes])
    y = tf.placeholder(tf.float32)

    # graphing
    loss_rate = []

    def nn(data):
        layer1 = {'w':tf.Variable(tf.random_normal([input_nodes, hl1_nodes])),
        layer2 = {'w':tf.Variable(tf.random_normal([hl1_nodes, hl2_nodes])),
        layer3 = {'w':tf.Variable(tf.random_normal([hl2_nodes, hl3_nodes])),
        output_layer = {'w':tf.Variable(tf.random_normal([hl3_nodes, output_nodes])),

        l1 = tf.add(tf.matmul(data, layer1['w']), layer1['b'])
        l1 = tf.nn.relu(l1)

        l2 = tf.add(tf.matmul(l1, layer2['w']), layer2['b'])
        l2 = tf.nn.relu(l2)

        l3 = tf.add(tf.matmul(l2, layer3['w']), layer3['b'])
        l3 = tf.nn.relu(l3)

        output = tf.add(tf.matmul(l3, output_layer['w']), output_layer['b'])


    def train(x):
        prediction = nn(x)
        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
        optimizer = tf.train.GradientDescentOptimizer(0.001).minimize(loss)

        init = tf.global_variables_initializer()

        for epoch in range(epochs):
            epochloss = 0
            batch_size = 10
            batches = 0
            for batch in range(int(len(x_train)/batch_size)):
                next_batch = batches+batch
                _, c = sess.run([optimizer, loss], feed_dict={x:x_train[batches:next_batch, :], y:y_train[batches:next_batch, :]})
                epochloss = epochloss + c
                batches += batch

            print("Epoch ", epoch, " / ", epochs, " - Loss ", epochloss)

        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
        accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
        print("Accuracy : ", accuracy.eval({x:x_test, y:y_test}))



2 Answers 2


Whereas unlikely to have high impact, I have found a potential source of overfitting in your code:

# lets normalize the data
def normalize(input_data):
    minimum = input_data.min(axis=0)
    maximum = input_data.max(axis=0)
    #normalized = (input_data - minimum) / ( maximum - minimum )
    normalized = preprocessing.normalize(input_data, norm='l2')
    return normalized

When training a model, you should always consider the complete pipeline. Everywhere, where dataset properties are used to adapt the pipeline, only training data should be used.

The preprocessing step - the normalization - needs to be trained as well. Therefore you would have to fit it with training data and the transform on test (without using the min and max of test data).

Data Leakage as in using test data properties in your model can result in overfitting.

See Medium and datascience.stackexchange for details such as:

Most practitioners — including myself — typically drop their full dataset into the same collection and normalize it all at once before splitting the data into test and evaluation. While the code for this approach will be cleaner, this breaks fundamental assumptions about data leakage. Most importantly, we are using information from data that will appear in both the test and training data. This is because our mean and standard deviation will be based on the full dataset, not just the training data.

Some practitioners will normalize the two datasets separately, using different means and standard deviations. This is also incorrect since it breaks the assumption that the data is drawn from the same distribution.

Mr. Guts tells us that in order to remedy this, we must first separate our data into training and test sets. Then, once we normalize the training set, we apply the mean and standard deviation to the normalization of the test set. This is a very subtle source of data leakage that most are apt to miss, but important to creating the best machine learning model possible.


I found the answer in rewriting the code as follows:

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

sess = tf.Session()

file = "mnist_train.csv"
data = np.loadtxt(file, delimiter=',')

y_vals = data[:,0:1]
x_vals = data[:,1:785]

seed = 3
batch_size = 90

# split into 80/20 datasets, normalize between 0:1 with min max scaling
train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False)
# up there we chose randomly 80% of the data
test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))
# up we chose the remaining 20% 

x_vals_train = x_vals[train_indices]
x_vals_test = x_vals[test_indices]
y_vals_train = y_vals[train_indices]
y_vals_test = y_vals[test_indices]

def normalize_cols(m):
    col_max = m.max(axis=0)
    col_min = m.min(axis=0)
    return (m-col_min)/(col_max - col_min)
x_vals_train = np.nan_to_num(normalize_cols(x_vals_train))
x_vals_test = np.nan_to_num(normalize_cols(x_vals_test))

# function that initializes the weights and the biases 
def init_weight(shape, std_dev):
    weight = tf.Variable(tf.random_normal(shape, stddev=std_dev))

def init_bias(shape, std_dev):
    bias= tf.Variable(tf.random_normal(shape, stddev=std_dev))

# initialize placeholders. 
x_data = tf.placeholder(shape=[None, 784], dtype=tf.float32)
y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)

# the fully connected layer will be used three times for all three hidden layers
def fully_connected(input_layer, weights, biases):
    layer = tf.add(tf.matmul(input_layer, weights), biases)
    return (tf.nn.relu(layer))

# Now create the model for each layer and the output layer.
# we will initialize a weight matrix, bias matrix and the fully connected layer
# for this, we will use hidden layers of size 500, 500, and 10

This will mean many variables variables to fit. This is because between the data and the first hidden layer we have 
784*500+500 = 392,500 variables to change.
continuing this way we will have end up with how many variables we have overall to fit

# create first layer (500 hidden nodes)
weight_1 = init_weight(shape=[784,500], std_dev=10.0)
bias_1 = init_bias(shape=[500], std_dev=10.0)
layer_1 = fully_connected(x_data, weight_1, bias_1)

# create second layer (5-- hidden nodes)
weight_2 = init_weight(shape=[500,500], std_dev=10.0)
bias_2 = init_bias(shape=[500], std_dev=10.0)
layer_2 = fully_connected(layer_1, weight_2, bias_2)

# create third layer (10 hidden nodes)
weight_3 = init_weight(shape=[500,10], std_dev=10.0)
bias_3 = init_bias(shape=[10], std_dev=10.0)
layer_3 = fully_connected(layer_2, weight_3, bias_3)

# create output layer (1 output value)
weight_4 = init_weight(shape=[10,1], std_dev=10.0)
bias_4 = init_bias(shape=[1], std_dev=10.0)
final_output = fully_connected(layer_3, weight_4, bias_4)

# define the loss function and the optimizer and initializing the model
loss = tf.reduce_mean(tf.abs(y_target - final_output))
optimizer = tf.train.AdamOptimizer(0.05)
train_step = optimizer.minimize(loss)

init = tf.global_variables_initializer()

# we will now train our model 10 times, store train and test los, select a random batch size, 
# and print the status every 1 generation

# initalize the loss vectors
loss_vec = []
test_loss = []
for i in range(10):
    # choose random indices for batch selection
    rand_index = np.random.choice(len(x_vals_train), size=batch_size)
    # get random batch
    rand_x = x_vals_train[rand_index]
    #rand_y = np.transpose(y_vals_train[rand_index])
    rand_y = y_vals_train[rand_index] #???????????
    # run the training step
    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
    # get and store train loss
    temp_loss = sess.run(loss, feed_dict={x_data:rand_x, y_target:rand_y})
    # get and store test loss 
    #test_temp_loss = sess.run(loss, feed_dict={x_data:x_vals_test, y_target:np.transpose([y_vals_test])})
    test_temp_loss = sess.run(loss, feed_dict={x_data:x_vals_test, y_target:y_vals_test}) #???????
    if(i+1) %1==0:
        print('Generation: '+str(i+1)+". Loss = "+str(temp_loss))

plt.plot(loss_vec, 'k-', label='Train Loss')
plt.plot(test_loss, 'r--', label='Test Loss')
plt.title('Loss Per generation ')
plt.legend(loc='upper right')

I commented most of it just so if someone stumbles here and needs some help they can understand whats going on.

  • 6
    \$\begingroup\$ "I found the answer in rewriting the code as follows" What answer did you find, do you have a summary? Is it the answer to whether the code is overfitting or not? \$\endgroup\$
    – Mast
    Apr 10, 2018 at 15:37

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