tensorflow - Why Spark ML perceptron classifier has high F1-score while the same model on TensorFlow performs very badly?

Question

Our team is working on a NLP problem. We have a dataset with some labeled sentences and we must classify them into two classes, 0 or 1.

We preprocess the data and use word embeddings so that we have 300 features for each sentence, then we use a simple neural network to train the model.

Since the data are very skewed we measure the model score with the F1-score, computing it both on the train set (80%) and the test set (20%).

Spark

We used the multilayer perceptron classifier featured in PySpark's MLlib:

layers = [300, 600, 2]

trainer = MultilayerPerceptronClassifier(featuresCol='features', labelCol='target',
                                         predictionCol='prediction', maxIter=10, layers=layers,
                                         blockSize=128)
model = trainer.fit(train_df)
result = model.transform(test_df)

predictionAndLabels = result.select("prediction", "target").withColumnRenamed("target", "label")
evaluator = MulticlassClassificationEvaluator(metricName="f1")
f1_score = evaluator.evaluate(predictionAndLabels)

This way we get F1-scores ranging between 0.91 and 0.93.

TensorFlow

We then chose to switch (mainly for learning purpose) to TensorFlow, so we implemented a neural network using the same architecture and formulas of the MLlib's one:

# Network Parameters
n_input = 300
n_hidden_1 = 600
n_classes = 2

# TensorFlow graph input
features = tf.placeholder(tf.float32, shape=(None, n_input), name='inputs')
labels = tf.placeholder(tf.float32, shape=(None, n_classes), name='labels')

# Initializes weights and biases
init_biases_and_weights()

# Layers definition
layer_1 = tf.add(tf.matmul(features, weights['h1']), biases['b1'])
layer_1 = tf.nn.sigmoid(layer_1)

out_layer = tf.matmul(layer_1, weights['out']) + biases['out']
out_layer = tf.nn.softmax(out_layer)

# Optimizer definition
learning_rate_ph = tf.placeholder(tf.float32, shape=(), name='learning_rate')
loss_function = tf.losses.log_loss(labels=labels, predictions=out_layer)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate_ph).minimize(loss_function)

# Start TensorFlow session
init = tf.global_variables_initializer()
tf_session = tf.InteractiveSession()
tf_session.run(init)

# Train Neural Network
learning_rate = 0.01
iterations = 100
batch_size = 256

total_batch = int(len(y_train) / batch_size)
for epoch in range(iterations):
    avg_cost = 0.0
    for block in range(total_batch):
        batch_x = x_train[block * batch_size:min(block * batch_size + batch_size, len(x_train)), :]
        batch_y = y_train[block * batch_size:min(block * batch_size + batch_size, len(y_train)), :]
        _, c = tf_session.run([optimizer, loss_function], feed_dict={learning_rate_ph: learning_rate,
                                                                     features: batch_x,
                                                                     labels: batch_y})
        avg_cost += c
    avg_cost /= total_batch
    print("Iteration " + str(epoch + 1) + " Logistic-loss=" + str(avg_cost))

# Make predictions
predictions_train = tf_session.run(out_layer, feed_dict={features: x_train, labels: y_train})
predictions_test = tf_session.run(out_layer, feed_dict={features: x_test, labels: y_test})

# Compute F1-score
f1_score = f1_score_tf(y_test, predictions_test)

Support functions:

def initialize_weights_and_biases():
    global weights, biases
    epsilon_1 = sqrt(6) / sqrt(n_input + n_hidden_1)
    epsilon_2 = sqrt(6) / sqrt(n_classes + n_hidden_1)
    weights = {
        'h1': tf.Variable(tf.random_uniform([n_input, n_hidden_1],
                                        minval=0 - epsilon_1, maxval=epsilon_1, dtype=tf.float32)),
        'out': tf.Variable(tf.random_uniform([n_hidden_1, n_classes],
                                         minval=0 - epsilon_2, maxval=epsilon_2, dtype=tf.float32))
    }
    biases = {
        'b1': tf.Variable(tf.constant(1, shape=[n_hidden_1], dtype=tf.float32)),
        'out': tf.Variable(tf.constant(1, shape=[n_classes], dtype=tf.float32))
    }

def f1_score_tf(actual, predicted):
    actual = np.argmax(actual, 1)
    predicted = np.argmax(predicted, 1)

    tp = tf.count_nonzero(predicted * actual)
    fp = tf.count_nonzero(predicted * (actual - 1))
    fn = tf.count_nonzero((predicted - 1) * actual)
    precision = tp / (tp + fp)
    recall = tp / (tp + fn)

    f1 = 2 * precision * recall / (precision + recall)
    return tf.Tensor.eval(f1)

This way we get F1-scores ranging between 0.24 and 0.25.

Question

The only differences that I can see between the two neural networks are:

• Optimizer: L-BFGS in Spark, Gradient Descent in TensorFlow
• Weights and biases initialization: Spark makes its own initialization while we initialize them manually in TensorFlow

I don't think that these two parameters can cause a so big difference in performance between the models, but still Spark seems to get very high scores in very few iterations.

I can't understand if TensorFlow is performing very bad or maybe Spark's scores are not truthful. And in both cases I think we aren't seeing something important.

标签： tensorflowmachine-learningneural-networkpysparkapache-spark-ml

Answer 1

Initializing weights as uniform and bias as 1 is certainly not a good idea, and it may very well be the cause of this discrepancy.

Use normal or truncated_normal instead, with the default zero mean and a small variance for the weights:

weights = {
        'h1': tf.Variable(tf.truncated_normal([n_input, n_hidden_1],
                                        stddev=0.01, dtype=tf.float32)),
        'out': tf.Variable(tf.truncated_normal([n_hidden_1, n_classes],
                                         stddev=0.01, dtype=tf.float32))
    }

and zero for the biases:

biases = {
        'b1': tf.Variable(tf.constant(0, shape=[n_hidden_1], dtype=tf.float32)),
        'out': tf.Variable(tf.constant(0, shape=[n_classes], dtype=tf.float32))
    }

That said, I am not sure about the correctness of using the MulticlassClassificationEvaluator for a binary classification problem, and I would suggest doing some further manual checks to confirm that the function indeed returns what you think it returns...