TensorFlow: Neural Network accuracy always 100% on train and test sets

Question

I created a TensorFlow neural network that has 2 hidden layers with 10 units each using ReLU activations and Xavier Initialization for the weights. The output layer has 1 unit outputting binary classification (0 or 1) using the sigmoid activation function to classify whether it believes a passenger on the titanic survived based on the input features.

(The only code omitted is the load_data function which populates the variables X_train, Y_train, X_test, Y_test used later in the program)

Parameters

# Hyperparams 
learning_rate = 0.001
lay_dims = [10,10, 1]

# Other params
m = X_train.shape[1] 
n_x = X_train.shape[0]
n_y = Y_train.shape[0]

Inputs

X = tf.placeholder(tf.float32, shape=[X_train.shape[0], None], name="X")
norm = tf.nn.l2_normalize(X, 0) # normalize inputs

Y = tf.placeholder(tf.float32, shape=[Y_train.shape[0], None], name="Y")

Initialize Weights & Biases

W1 = tf.get_variable("W1", [lay_dims[0],n_x], initializer=tf.contrib.layers.xavier_initializer())
b1 = tf.get_variable("b1", [lay_dims[0],1], initializer=tf.zeros_initializer())

W2 = tf.get_variable("W2", [lay_dims[1],lay_dims[0]], initializer=tf.contrib.layers.xavier_initializer())
b2 = tf.get_variable("b2", [lay_dims[1],1], initializer=tf.zeros_initializer())

W3 = tf.get_variable("W3", [lay_dims[2],lay_dims[1]], initializer=tf.contrib.layers.xavier_initializer())
b3 = tf.get_variable("b3", [lay_dims[2],1], initializer=tf.zeros_initializer())

Forward Prop

Z1 = tf.add(tf.matmul(W1,X), b1)
A1 = tf.nn.relu(Z1)

Z2 = tf.add(tf.matmul(W2,A1), b2)
A2 = tf.nn.relu(Z2)

Y_hat = tf.add(tf.matmul(W3,A2), b3)

BackProp

cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=tf.transpose(Y_hat), labels=tf.transpose(Y)))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

Session

# Initialize
init = tf.global_variables_initializer()

with tf.Session() as sess:
    # Initialize
    sess.run(init)

    # Normalize Inputs
    sess.run(norm, feed_dict={X:X_train, Y:Y_train})

    # Forward/Backprob and update weights
    for i in range(10000):
        c, _ = sess.run([cost, optimizer], feed_dict={X:X_train, Y:Y_train})
        if i % 100 == 0:
            print(c)

    correct_prediction = tf.equal(tf.argmax(Y_hat), tf.argmax(Y))

    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

    print("Training Set:", sess.run(accuracy, feed_dict={X: X_train, Y: Y_train}))
    print("Testing Set:", sess.run(accuracy, feed_dict={X: X_test, Y: Y_test}))

After running running 10,000 epochs of training, the cost goes down each time so it shows that the learning_rate is okay and that the cost function appears normal. However, after training, all of my Y_hat values (predictions on the training set) are 1 (predicting the passenger survived). So basically the prediction just outputs y=1 for every training example.

Also, when I run tf.argmax on Y_hat, the result is a matrix of all 0's. The same thing is happening when tf.argmax is applied to Y (ground truth labels) which is odd because Y consists of all the correct labels for the training examples.

Any help is greatly appreciated. Thanks.

I do not understand "It seems that all of my data coming from Y_hat are 1 or near 1 the longer I train the model and all of my values of argmax on Y_hat and on Y (which has the ground truth labels of 0 or 1) come out as 0. " The sentence is quite confusing. Can you rephrase it please? — Lan

Lan Lan · Accepted Answer · 2017-09-13T03:56:34

I assume your Y_hat is a (1,m) matrix with m is the number of training example. Then the tf.argmax(Y_hat) will give all 0. According to tensorflow documentation, argmax

Returns the index with the largest value across axes of a tensor.

If you do not pass in axis, the axis is set as 0. Because the axis 0 only has one value, the returned index becomes 0 all the time.

TensorFlow: Neural Network accuracy always 100% on train and test sets

2 Answers