我正在尝试使用TensorFlow Keras API实现多实例学习模型。假设输入张量形状为(None, 18, 10, 300)，并且我需要使用submodel和axis=1进行多实例学习。因此，对submodel的输入应为(None, 10, 300)。假设单个submodel的输出张量形状为(None, 100)，然后将submodel s的输出串联起来，从而创建输出形状(None, 18, 100)。
实现1.此实现用keras Lambda层分割输入张量，执行submodel，在axis=1中扩展结果的维，然后将结果沿{{1}连接起来}。
axis=1实现2。此实现使用def get_branch_model(input_shape, submodel, args={}):
model_input = tf.keras.Input(input_shape)
sliced_inputs = [tf.keras.layers.Lambda(lambda x: x[:,i])(model_input)
for i in range(input_shape[0])]
sub_instance = submodel(**args)
branch_models = [sub_instance(sliced_inputs[i]) for i in range(input_shape[0])]
expand_layer = tf.keras.layers.Lambda(lambda x: tf.keras.backend.expand_dims(x, axis=1))
expanded_outputs = [expand_layer(branch_models[i]) for i in range(input_shape[0])]
concated_layer = tf.keras.layers.Concatenate(axis=1)(expanded_outputs)
return tf.keras.Model(model_input, concated_layer)
拆分输入张量，执行tf.slice，在submodel中扩展结果的维，然后将结果沿axis=1连接。
axis=1参数
def __get_filter_layer(total_dim, target_dim, index): def tensor_filter(tensor_in): nonlocal index begin = [0 if i != target_dim else index for i in range(total_dim)] size = [-1 if i != target_dim else 1 for i in range(total_dim)] return tf.squeeze(tf.slice(tensor_in, begin, size), axis=target_dim) return tf.keras.models.Sequential([ tf.keras.layers.Lambda(tensor_filter) ]) def get_branch_model(input_shape, branch_index, output_shape, submodel, args={}): model_input = tf.keras.Input(input_shape) sliced_inputs = [__get_filter_layer(len(input_shape) + 1, branch_index, i)(model_input) for i in range(input_shape[branch_index - 1])] sub_instance = submodel(**args) branch_models = [sub_instance(sliced_inputs[i]) for i in range(input_shape[branch_index - 1])] expand_layer = tf.keras.layers.Lambda(lambda x: tf.keras.backend.expand_dims(x, axis=1)) expanded_outputs = [expand_layer(branch_models[i]) for i in range(input_shape[0])] concated_layer = tf.keras.layers.Concatenate(axis=1)(expanded_outputs) return tf.keras.Model(model_input, concated_layer)的输入为input_shape，而(18, 10, 300)的输入为branch_index。1是一个顺序模型，例如submodel。
答案 0 :(得分：1)
您的多实例学习模型是否基于特定的参考模型/论文？我根据本文https://mdenil.com/static/papers/2015-deep-multi-instance-learning.pdf
编写了一个玩具实现# mitr.py
"""
weakly supervised learning of a
linear boundary
"""
import os
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import sys
def mitr(data_gen, batch_size, input_dim, ngroups):
input = tf.placeholder(tf.float32, [None, input_dim])
# fraction of training sample containing positive instances
pos_class_ratio = tf.placeholder(tf.float32, name='pos_class_ratio')
# group indices present
group_labels_input = tf.placeholder(tf.int32, [None])
# sort inputs by group label index
values, indices = tf.nn.top_k(group_labels_input, k=tf.shape(group_labels_input)[0], sorted=True)
input = tf.gather(input, indices[::-1], axis=0)
unique_group_labels_input, unique_group_enum = tf.unique(values[::-1])
global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')
def lr(x):
w = tf.Variable(tf.random_normal((2, 1)), dtype=tf.float32)
b = tf.Variable(tf.random_normal((1,)), dtype=tf.float32)
return tf.nn.sigmoid(tf.add(tf.matmul(x, w), b))
def gpp(l, lh):
l = tf.expand_dims(l, -1)
lh = tf.expand_dims(lh, -1)
l_sq = tf.reduce_sum(tf.square(l))
lh_sq = tf.reduce_sum(tf.square(lh))
l_lh_dp = tf.matmul(tf.transpose(l), lh)
return tf.reduce_sum(l_sq - 2 * l_lh_dp + lh_sq)
def pp(y):
return pr_sq_diff(y)
def pr_sq_diff(x):
x_sq = tf.reduce_sum(tf.square(x), axis=-1)
x_dp = tf.matmul(x, tf.transpose(x))
return tf.abs(x_sq - 2 * x_dp + tf.transpose(x_sq))
def rbf_kernal(x):
gamma = 1.0
return tf.exp(-gamma * pr_sq_diff(x))
def aggpred(y):
return tf.segment_mean(y[:, 0], unique_group_enum)
N = tf.constant(batch_size, dtype=tf.float32)
lambda_c = tf.constant(1.0, dtype=tf.float32)
K = tf.constant(ngroups, dtype=tf.float32)
ypred = lr(input)
loss_1 = 1.0 / tf.square(N) * tf.reduce_sum(rbf_kernal(input) * pp(ypred))
loss_2 = lambda_c / K * gpp(tf.cast(unique_group_labels_input, tf.float32), aggpred(ypred))
loss = loss_1 + loss_2
optimizer = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(loss, global_step=global_step)
writer = tf.summary.FileWriter("mitr_logs")
saver = tf.train.Saver()
with tf.name_scope("summaries"):
tf.summary.scalar("loss", loss)
tf.summary.scalar("loss1", loss_1)
tf.summary.scalar("loss2", loss_2)
tf.summary.histogram("positive class ratio", pos_class_ratio)
summary_op = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
cpkt = tf.train.get_checkpoint_state(os.path.dirname("mitr" + "/checkpoint"))
if cpkt and cpkt.model_checkpoint_path:
saver.restore(sess, cpkt.model_checkpoint_path)
print("Loaded checkpointed model:\n {} for {}.".format(cpkt.model_checkpoint_path,
"mitr"))
fig, axes = plt.subplots(nrows=4, ncols=4)
fig.set_size_inches(11, 20)
plt_ix = 0
for X, group_labels, y, ratio in data_gen(batch_size):
_, ls, ls1, ls2, step, summary, posrt = sess.run([optimizer, loss, loss_1, loss_2, global_step, summary_op, pos_class_ratio],
feed_dict={input: X, group_labels_input: group_labels, pos_class_ratio: ratio})
if step % 300 == 0:
print("Loss: {} Loss1: {} Loss2: {}".format(ls, ls1, ls2))
print("Fraction of sample data in positive class: {}".format(ratio))
saver.save(sess, "mitr" + "/" + "mitr", global_step=step)
writer.add_summary(summary, step)
writer.flush()
print("Saved to {}".format("mitr" + "/" + "mitr"))
heatmap_pred = sess.run(ypred, feed_dict={input: heatmap_input(), group_labels_input: group_labels})
heatmap(heatmap_pred, axes.flatten()[plt_ix])
axes.flatten()[plt_ix].set_title("step: {}".format(step))
plt_ix += 1
if plt_ix > 15:
plt.savefig('heatmap.png', dpi=100)
print("Saved heatmap.png")
sys.exit(0)
def sample_decision_boundary_small_uniform(batch_size):
""" Sample random small squares uniformly to learn
linear decision surface in multi-instance learning. """
def boundary_label(e):
if e[1] + e[0] > 0:
return 1
return 0
sq_len = 1.5
while True:
p = np.random.uniform(-5, 5, size=(2))
x = np.random.uniform(0, sq_len, size=(100, 2)) + p
y = np.apply_along_axis(boundary_label, 1, x).astype(np.float32)
n_in_group = np.sum(y)
ratio = n_in_group * 1.0 / batch_size
y_group = np.zeros((batch_size,))
y_group[:] = ratio
yield x, y_group, y, ratio
def heatmap(heatmap_values, axes):
""" heatmap_values is given in order rows then
columns: 00, 01, 02,..etc
"""
n = 100
data = np.zeros(shape=(n, n))
for i in range(n):
for j in range(n):
data[(i, j)] = heatmap_values[n * i + j]
axes.pcolor(data, cmap=plt.cm.Blues)
def heatmap_input():
l = 10.0
n = 100
column_labels = np.linspace(-l, l, n)
row_labels = np.linspace(-l, l, n)
x = np.zeros(shape=(n**2, 2))
for i in range(n):
for j in range(n):
x[i * n + j] = [row_labels[i], column_labels[j]]
return x
if __name__ == "__main__":
mitr(sample_decision_boundary_small_uniform, 100, 2, 2)