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@racinmat
Last active October 17, 2019 09:52
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Unsorted segmented max with eager execution is not working.
import pandas as pd
import tensorflow as tf
import numpy as np
from sklearn.preprocessing import MultiLabelBinarizer
class SegmentedMean(tf.keras.layers.Layer):
def __init__(self, *args, **kwargs):
super(SegmentedMean, self).__init__(*args, **kwargs)
def call(self, inputs, **kwargs):
features, segments, num_segments = inputs
# return tf.math.segment_mean(features, segments)
return tf.math.unsorted_segment_mean(features, segments, num_segments)
class SegmentedMax(tf.keras.layers.Layer):
def __init__(self, none_val=None, *args, **kwargs):
super(SegmentedMax, self).__init__(*args, **kwargs)
self.none_val = none_val
def call(self, inputs, **kwargs):
features, segments, num_segments = inputs
# return tf.math.segment_max(features, segments)
return tf.math.unsorted_segment_max(features, segments, num_segments)
df = pd.DataFrame({'list': [
[[1, 2, 3, 4], [2, 3, 6]],
[[1, 2], [1, 5, 8]],
[[6, 7, 8], [2, 4, 10], [1, 6], [5]],
[[3, 4, 6, 8], [1, 8], [2], [7]],
[[3, 6, 8]],
[[4, 2, 2], [8, 1, 5, 6]],
[[3, 2, 1], [9, 8], [4]],
],
'label': [0, 0, 1, 1, 1, 0, 0]})
df['list_len'] = df['list'].apply(len)
batch_size = 8
# list of all unique numbers used
nums_used = list(set([i for j in df['list'].apply(lambda k: [i for j in k for i in j]) for i in j]))
list_enc = MultiLabelBinarizer().fit([[i] for i in nums_used])
def create_batch():
batch_ub = df.sample(batch_size // 2) # upper bound, will trim so the batch size is correct after expansion
batch_end = (batch_ub['list_len'].cumsum() <= batch_size)[::-1].idxmax()
batch = batch_ub.loc[:batch_end].copy()
batch['id'] = np.arange(0, len(batch))
feat_bags = batch['list'].apply(list_enc.transform)
feats = np.concatenate(feat_bags.values)
# so I know which data to group together during segmentation
segments = batch['id'].repeat(batch['list_len']).values
labels = batch['label'].values.astype(np.int32)
return (feats, segments, np.array([len(labels)], dtype=np.int32)), labels
settings = {'k': 40, 'steps': 10}
feats_len = len(nums_used)
inputs = tf.keras.Input(shape=(feats_len,), name='features')
segments = tf.keras.Input(shape=(), name='segments', dtype=tf.int32)
samples_num = tf.keras.Input(shape=(), name='samples_num', dtype=tf.int32)
x = tf.keras.layers.Dense(settings['k'], activation=tf.nn.relu)(inputs)
x = tf.keras.layers.Dense(settings['k'])(x)
# x = SegmentedMean()((x, segments, samples_num[0])) # unsorted segmented mean works without problems
x = SegmentedMax()((x, segments, samples_num[0]))
x = tf.keras.layers.Dense(settings['k'], activation=tf.nn.relu)(x)
logits = tf.keras.layers.Dense(2, name='output_logits')(x)
probs = tf.keras.layers.Softmax()(logits)
model = tf.keras.Model(inputs=(inputs, segments, samples_num), outputs=(logits, probs), name='mil_model')
optimizer = tf.keras.optimizers.Adam()
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
for step in range(settings['steps']):
x_train, y_train = create_batch()
with tf.GradientTape() as tape:
logits, probs = model(x_train)
loss_value = loss(y_train, logits)
grads = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
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