def inference(input_placeholder):
with tf.name_scope("inference") as scope:
W = tf.Variable(tf.zeros([3, 3]), name="weight")
b = tf.Variable(tf.zeros([3]), name="bias")
y = tf.nn.softmax(tf.matmul(input_placeholder, W) + b)
return y
def __init__(self, input_size, num_hidden, minibatch_size, name='lstmcell'):
"""
Constructs an LSTM Cell
Parameters:
----------
input_size: int
the size of the single input vector to the cell
num_hidden: int
the number of hidden nodes in the cell
minibatch_size: int
the number of the input vectors in the input matrix
"""
LSTMCell.created_count += 1
self.id = name + '_' + str(LSTMCell.created_count) if name == 'lstmcell' else name
self.input_size = input_size
self.num_hidden = num_hidden
self.minibatch_size = minibatch_size
self.input_weights = tf.Variable(tf.truncated_normal([self.input_size, self.num_hidden * 4], -0.1, 0.1), name=self.id + '_wi')
self.output_weights = tf.Variable(tf.truncated_normal([self.num_hidden, self.num_hidden * 4], -0.1, 0.1), name=self.id + '_wo')
self.bias = tf.Variable(tf.zeros([self.num_hidden * 4]), name=self.id + '_b')
self.prev_output = tf.Variable(tf.zeros([self.minibatch_size, self.num_hidden]), trainable=False, name=self.id+'_o')
self.prev_state = tf.Variable(tf.zeros([self.minibatch_size, self.num_hidden]), trainable=False, name=self.id+'_s')
def testGradient(self):
with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()) as sess:
batch_size = 1
cell_size = 3
input_size = 2
# Inputs
x = tf.zeros([batch_size, input_size])
h = tf.zeros([batch_size, cell_size])
output = gru_ops.GRUBlockCell(cell_size)(x, h)
sess.run([tf.initialize_all_variables()])
all_variables = tf.all_variables()
[w_ru, b_ru, w_c, b_c] = all_variables[:4]
error_x = tf.test.compute_gradient_error(x, (batch_size, input_size), output[0], (batch_size, cell_size))
error_h = tf.test.compute_gradient_error(h, (batch_size, cell_size), output[0], (batch_size, cell_size))
error_w_ru = tf.test.compute_gradient_error(
w_ru, (input_size + cell_size, 2 * cell_size), output[0], (batch_size, cell_size)
)
error_w_c = tf.test.compute_gradient_error(
w_c, (input_size + cell_size, cell_size), output[0], (batch_size, cell_size)
)
error_b_ru = tf.test.compute_gradient_error(b_ru, (2 * cell_size,), output[0], (batch_size, cell_size))
error_b_c = tf.test.compute_gradient_error(b_c, (cell_size,), output[0], (batch_size, cell_size))
eps = 1e-4
self.assertLess(error_x, eps)
self.assertLess(error_h, eps)
self.assertLess(error_w_ru, eps)
self.assertLess(error_w_c, eps)
self.assertLess(error_b_ru, eps)
self.assertLess(error_b_c, eps)
def make_variable_dict(max_age, max_gender):
# TODO(sibyl-toe9oF2e): Figure out how to derive max_age & max_gender from
# examples_dict.
age_weights = tf.Variable(tf.zeros([max_age + 1], dtype=tf.float32))
gender_weights = tf.Variable(tf.zeros([max_gender + 1], dtype=tf.float32))
return dict(sparse_features_weights=[age_weights, gender_weights],
dense_features_weights=[])
def testBlockGRUToGRUCellSingleStep(self):
with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()) as sess:
batch_size = 4
cell_size = 5
input_size = 6
seed = 1994
initializer = tf.random_uniform_initializer(-0.01, 0.01, seed=seed)
# Inputs
x = tf.zeros([batch_size, input_size])
h = tf.zeros([batch_size, cell_size])
# Values for the inputs.
x_value = np.random.rand(batch_size, input_size)
h_value = np.random.rand(batch_size, cell_size)
# Output from the basic GRU cell implementation.
with tf.variable_scope("basic", initializer=initializer):
output = tf.nn.rnn_cell.GRUCell(cell_size)(x, h)
sess.run([tf.initialize_all_variables()])
basic_res = sess.run([output], {x: x_value, h: h_value})
# Output from the block GRU cell implementation.
with tf.variable_scope("block", initializer=initializer):
output = gru_ops.GRUBlockCell(cell_size)(x, h)
sess.run([tf.initialize_all_variables()])
block_res = sess.run([output], {x: x_value, h: h_value})
self.assertEqual(len(block_res), len(basic_res))
for block, basic in zip(block_res, basic_res):
self.assertAllClose(block, basic)
def moving_average(value, window):
value = tf.to_float(value)
shape = value.get_shape()
queue_init = tf.zeros(tf.TensorShape(window).concatenate(shape))
total_init = tf.zeros(shape)
num_init = tf.constant(0, dtype=tf.float32)
queue = tf.FIFOQueue(window, [tf.float32], shapes=[shape])
total = tf.Variable(total_init, trainable=False)
num = tf.Variable(num_init, trainable=False)
init = tf.cond(
tf.equal(queue.size(), 0),
lambda: tf.group(
queue.enqueue_many(queue_init),
total.assign(total_init),
num.assign(num_init)),
lambda: tf.no_op())
with tf.control_dependencies([init]):
total_ = total + value - queue.dequeue()
num_ = num + 1
value_averaged = total_ / (tf.minimum(num_, window) + EPSILON)
with tf.control_dependencies([queue.enqueue([value]), total.assign(total_), num.assign(num_)]):
return tf.identity(value_averaged)
def __init__(self, dim_image, n_words, dim_hidden, batch_size, n_lstm_steps, drop_out_rate, bias_init_vector=None):
self.dim_image = dim_image
self.n_words = n_words
self.dim_hidden = dim_hidden
self.batch_size = batch_size
self.n_lstm_steps = n_lstm_steps
self.drop_out_rate = drop_out_rate
with tf.device("/cpu:0"):
self.Wemb = tf.Variable(tf.random_uniform([n_words, dim_hidden], -0.1, 0.1), name='Wemb')
#self.Wemb_W = tf.Variable(tf.random_uniform([n_words, dim_hidden], -0.1, 0.1), name='Wemb_W')
#self.Wemb_b = tf.Variable(tf.random_uniform([dim_hidden], -0.1, 0.1), name='Wemb_b')
#self.lstm3 = rnn_cell.BasicLSTMCell(dim_hidden)
self.lstm3 = rnn_cell.LSTMCell(self.dim_hidden,2*self.dim_hidden,use_peepholes = True)
self.lstm3_dropout = rnn_cell.DropoutWrapper(self.lstm3,output_keep_prob=1 - self.drop_out_rate)
self.encode_image_W = tf.Variable( tf.random_uniform([dim_image, dim_hidden], -0.1, 0.1), name='encode_image_W')
self.encode_image_b = tf.Variable( tf.zeros([dim_hidden]), name='encode_image_b')
self.embed_att_w = tf.Variable(tf.random_uniform([dim_hidden, 1], -0.1,0.1), name='embed_att_w')
self.embed_att_Wa = tf.Variable(tf.random_uniform([dim_hidden, dim_hidden], -0.1,0.1), name='embed_att_Wa')
self.embed_att_Ua = tf.Variable(tf.random_uniform([dim_hidden, dim_hidden],-0.1,0.1), name='embed_att_Ua')
self.embed_att_ba = tf.Variable( tf.zeros([dim_hidden]), name='embed_att_ba')
self.embed_word_W = tf.Variable(tf.random_uniform([dim_hidden, n_words], -0.1,0.1), name='embed_word_W')
if bias_init_vector is not None:
self.embed_word_b = tf.Variable(bias_init_vector.astype(np.float32), name='embed_word_b')
else:
self.embed_word_b = tf.Variable(tf.zeros([n_words]), name='embed_word_b')
self.embed_nn_Wp = tf.Variable(tf.random_uniform([3*dim_hidden, dim_hidden], -0.1,0.1), name='embed_nn_Wp')
self.embed_nn_bp = tf.Variable(tf.zeros([dim_hidden]), name='embed_nn_bp')
def inference(images, hidden1_units):
"""Build the MNIST model up to where it may be used for inference.
Args:
images: Images placeholder, from inputs().
hidden1_units: Size of the first hidden layer.
hidden2_units: Size of the second hidden layer.
Returns:
softmax_linear: Output tensor with the computed logits.
"""
# Hidden 1
with tf.name_scope('hidden1'):
weights = tf.Variable(
tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
name='weights')
biases = tf.Variable(tf.zeros([hidden1_units]),
name='biases')
hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
# Hidden 2
# Linear
with tf.name_scope('softmax_linear'):
weights = tf.Variable(
tf.truncated_normal([hidden1_units, NUM_CLASSES],
stddev=1.0 / math.sqrt(float(hidden1_units))),
name='weights')
biases = tf.Variable(tf.zeros([NUM_CLASSES]),
name='biases')
logits = tf.matmul(hidden1, weights) + biases
return logits
def get_idx_map(shape):
"""Get index map for a image.
Args:
shape: [B, T, H, W] or [B, H, W]
Returns:
idx: [B, T, H, W, 2], or [B, H, W, 2]
"""
s = shape
ndims = tf.shape(s)
wdim = ndims - 1
hdim = ndims - 2
idx_shape = tf.concat(0, [s, tf.constant([1])])
ones_h = tf.ones(hdim - 1, dtype='int32')
ones_w = tf.ones(wdim - 1, dtype='int32')
h_shape = tf.concat(0, [ones_h, tf.constant([-1]), tf.constant([1, 1])])
w_shape = tf.concat(0, [ones_w, tf.constant([-1]), tf.constant([1])])
idx_y = tf.zeros(idx_shape, dtype='float')
idx_x = tf.zeros(idx_shape, dtype='float')
h = tf.slice(s, ndims - 2, [1])
w = tf.slice(s, ndims - 1, [1])
idx_y += tf.reshape(tf.to_float(tf.range(h[0])), h_shape)
idx_x += tf.reshape(tf.to_float(tf.range(w[0])), w_shape)
idx = tf.concat(ndims[0], [idx_y, idx_x])
return idx
def testSampleFromDiscretizedMixLogistic(self):
batch = 2
height = 4
width = 4
num_mixtures = 5
seed = 42
logits = tf.concat( # assign all probability mass to first component
[tf.ones([batch, height, width, 1]) * 1e8,
tf.zeros([batch, height, width, num_mixtures - 1])],
axis=-1)
locs = tf.random_uniform([batch, height, width, num_mixtures * 3],
minval=-.9, maxval=.9)
log_scales = tf.ones([batch, height, width, num_mixtures * 3]) * -1e8
coeffs = tf.atanh(tf.zeros([batch, height, width, num_mixtures * 3]))
pred = tf.concat([logits, locs, log_scales, coeffs], axis=-1)
locs_0 = locs[..., :3]
expected_sample = tf.clip_by_value(locs_0, -1., 1.)
actual_sample = common_layers.sample_from_discretized_mix_logistic(
pred, seed=seed)
actual_sample_val, expected_sample_val = self.evaluate(
[actual_sample, expected_sample])
# Use a low tolerance: samples numerically differ, as the actual
# implementation clips log-scales so they always contribute to sampling.
self.assertAllClose(actual_sample_val, expected_sample_val, atol=1e-2)
def __init__(self, dim_image, n_words, dim_hidden, batch_size, n_lstm_steps, drop_out_rate, bias_init_vector=None):
self.dim_image = dim_image
self.n_words = n_words
self.dim_hidden = dim_hidden
self.batch_size = batch_size
self.n_lstm_steps = n_lstm_steps
self.drop_out_rate = drop_out_rate
with tf.device("/gpu:2"):
self.Wemb = tf.Variable(tf.random_uniform([n_words, dim_hidden], -0.1, 0.1), name='Wemb')
# self.lstm1 = rnn_cell.BasicLSTMCell(dim_hidden)
# self.lstm2 = rnn_cell.BasicLSTMCell(dim_hidden)
self.lstm1 = rnn_cell.LSTMCell(self.dim_hidden,self.dim_hidden,use_peepholes = True)
self.lstm1_dropout = rnn_cell.DropoutWrapper(self.lstm1,output_keep_prob=1 - self.drop_out_rate)
self.lstm2 = rnn_cell.LSTMCell(self.dim_hidden,self.dim_hidden,use_peepholes = True)
self.lstm2_dropout = rnn_cell.DropoutWrapper(self.lstm2,output_keep_prob=1 - self.drop_out_rate)
# W is Weight, b is Bias
self.encode_image_W = tf.Variable( tf.random_uniform([dim_image, dim_hidden], -0.1, 0.1), name='encode_image_W')
self.encode_image_b = tf.Variable( tf.zeros([dim_hidden]), name='encode_image_b')
self.embed_word_W = tf.Variable(tf.random_uniform([dim_hidden, n_words], -0.1,0.1), name='embed_word_W')
if bias_init_vector is not None:
self.embed_word_b = tf.Variable(bias_init_vector.astype(np.float32), name='embed_word_b')
else:
self.embed_word_b = tf.Variable(tf.zeros([n_words]), name='embed_word_b')
def __init__(self, n_input, n_latent, n_hidden_enc):
# initialize network
self.prng = numpy.random.RandomState()
sigma_init = 0.01
x = tf.placeholder(tf.float32, [None, n_input], name='input')
# encoder
# x->hidden layer
W_xh = tf.Variable(tf.random_normal([n_input, n_hidden_enc],
mean=0., stddev=sigma_init, dtype=tf.float32))
b_xh = tf.Variable(tf.zeros([n_hidden_enc], dtype=tf.float32))
# hidden layer -> latent variables (mu & log sigma^2)
W_hmu = tf.Variable(tf.random_normal([n_hidden_enc, n_latent],
mean=0., stddev=sigma_init, dtype=tf.float32))
b_hsigma = tf.Variable(tf.zeros([n_latent], dtype=tf.float32))
# decoder
W_zx = tf.Variable(tf.random_normal([n_latent, n_input],
mean=0., stddev=sigma_init, dtype=tf.float32))
b_zx = tf.Variable(tf.zeros([n_input], dtype=tf.float32))
# create functions
h_encoder = tf.nn.relu(tf.mat_mul(x, W_xh) + b_xh)
pass
def testDiscretizedMixLogisticLoss(self):
batch = 2
height = 4
width = 4
channels = 3
num_mixtures = 5
logits = tf.concat( # assign all probability mass to first component
[tf.ones([batch, height, width, 1]) * 1e8,
tf.zeros([batch, height, width, num_mixtures - 1])],
axis=-1)
locs = tf.random_uniform([batch, height, width, num_mixtures * 3],
minval=-.9, maxval=.9)
log_scales = tf.random_uniform([batch, height, width, num_mixtures * 3],
minval=-1., maxval=1.)
coeffs = tf.atanh(tf.zeros([batch, height, width, num_mixtures * 3]))
pred = tf.concat([logits, locs, log_scales, coeffs], axis=-1)
# Test labels that don't satisfy edge cases where 8-bit value is 0 or 255.
labels = tf.random_uniform([batch, height, width, channels],
minval=-.9, maxval=.9)
locs_0 = locs[..., :3]
log_scales_0 = log_scales[..., :3]
centered_labels = labels - locs_0
inv_stdv = tf.exp(-log_scales_0)
plus_in = inv_stdv * (centered_labels + 1. / 255.)
min_in = inv_stdv * (centered_labels - 1. / 255.)
cdf_plus = tf.nn.sigmoid(plus_in)
cdf_min = tf.nn.sigmoid(min_in)
expected_loss = -tf.reduce_sum(tf.log(cdf_plus - cdf_min), axis=-1)
actual_loss = common_layers.discretized_mix_logistic_loss(
pred=pred, labels=labels)
actual_loss_val, expected_loss_val = self.evaluate(
[actual_loss, expected_loss])
self.assertAllClose(actual_loss_val, expected_loss_val, rtol=1e-5)
def recover_feeling(checkpoint):
#设置变量
in_sentence = tf.placeholder(tf.float32, [None, 140])
weight = tf.Variable(tf.zeros([140, 6]))
biases = tf.Variable(tf.zeros([6]))
global_step = tf.Variable(0, name='global_step', trainable=False)
#y = softmax(Wx + b)
y = tf.nn.softmax(tf.matmul(in_sentence, weight) + biases)
y_ = tf.placeholder(tf.float32, [None, 6])
sess = tf.InteractiveSession()
#恢复模型.
saver = tf.train.Saver()
saver.restore(sess, checkpoint)
#读取模型完毕，加载词表
vocab, tmp_vocab = read_vocabulary('data/emotion/vocabulary.txt')
#把一些句子转化为vec的array
vec_list1 = convert_sentence_to_vec_list('你今天感觉怎么样', vocab, 140)
vec_list2 = convert_sentence_to_vec_list('高兴啊', vocab, 140)
vec_list_final = [np.array(vec_list1), np.array(vec_list2)]
print (vec_list_final)
#交给sess进行检查
data = np.array(vec_list_final)
result = sess.run(y, feed_dict={in_sentence: data})
print (result)
def main():
sess = tf.Session()
# 2進数3ビットから10進数
x = tf.placeholder(tf.float32, [None, 3])
w = tf.Variable(tf.zeros([3, 8]))
b = tf.Variable(tf.zeros([8]))
y = tf.nn.softmax(tf.matmul(x, w) + b)
y_ = tf.placeholder(tf.float32, [None, 8])
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)
sess.run(tf.initialize_all_variables())
for i in range(1000):
train_step.run({x: [[0, 0, 0]], y_: [[1, 0, 0, 0, 0, 0, 0, 0]]}, session=sess)
train_step.run({x: [[1, 0, 0]], y_: [[0, 1, 0, 0, 0, 0, 0, 0]]}, session=sess)
train_step.run({x: [[0, 1, 0]], y_: [[0, 0, 1, 0, 0, 0, 0, 0]]}, session=sess)
train_step.run({x: [[1, 1, 0]], y_: [[0, 0, 0, 1, 0, 0, 0, 0]]}, session=sess)
train_step.run({x: [[0, 0, 1]], y_: [[0, 0, 0, 0, 1, 0, 0, 0]]}, session=sess)
train_step.run({x: [[1, 0, 1]], y_: [[0, 0, 0, 0, 0, 1, 0, 0]]}, session=sess)
train_step.run({x: [[0, 1, 1]], y_: [[0, 0, 0, 0, 0, 0, 1, 0]]}, session=sess)
train_step.run({x: [[1, 1, 1]], y_: [[0, 0, 0, 0, 0, 0, 0, 1]]}, session=sess)
## 1に近い予測があってるか 平均
#correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
#accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(y, feed_dict={x: [[0, 0, 0]]}))
print(sess.run(y, feed_dict={x: [[1, 0, 0]]}))
print(sess.run(y, feed_dict={x: [[0, 1, 0]]}))
print(sess.run(y, feed_dict={x: [[1, 1, 0]]}))
print(sess.run(y, feed_dict={x: [[0, 0, 1]]}))
return 0
def testLSTMFusedCell(self):
with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()) as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m0 = tf.zeros([1, 2])
m1 = tf.zeros([1, 2])
m2 = tf.zeros([1, 2])
m3 = tf.zeros([1, 2])
g, ((out_m0, out_m1), (out_m2, out_m3)) = tf.nn.rnn_cell.MultiRNNCell(
[tf.contrib.rnn.LSTMFusedCell(2)] * 2,
state_is_tuple=True)(x, ((m0, m1), (m2, m3)))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, out_m0, out_m1, out_m2, out_m3],
{x.name: np.array([[1., 1.]]),
m0.name: 0.1 * np.ones([1, 2]),
m1.name: 0.1 * np.ones([1, 2]),
m2.name: 0.1 * np.ones([1, 2]),
m3.name: 0.1 * np.ones([1, 2])})
self.assertEqual(len(res), 5)
self.assertAllClose(res[0], [[0.24024698, 0.24024698]])
# These numbers are from testBasicLSTMCell and only test c/h.
self.assertAllClose(res[1], [[0.68967271, 0.68967271]])
self.assertAllClose(res[2], [[0.44848421, 0.44848421]])
self.assertAllClose(res[3], [[0.39897051, 0.39897051]])
self.assertAllClose(res[4], [[0.24024698, 0.24024698]])
def autoencoder_contd(input_dim, representation):
x = tf.placeholder(tf.float32, [None, input_dim]);
high_decW=tf.Variable(
initial_value=tf.random_normal(
[representation,input_dim],
-math.sqrt(6.0/(input_dim+representation)),
math.sqrt(6.0/(input_dim+representation))),
dtype=tf.float32,
name='high_decW');
# high_encW=tf.transpose(high_decW);
high_encW=tf.Variable(
initial_value=tf.random_normal(
[input_dim, representation],
-math.sqrt(6.0/(input_dim+representation)),
math.sqrt(6.0/(input_dim+representation))),
name='high_encW');
high_encb=tf.Variable(tf.zeros([representation]),
name='high_encb');
z=tf.nn.sigmoid(tf.matmul(x,high_encW) + high_encb);
hidden_weights=high_encW;
high_decb=tf.Variable(
tf.zeros([input_dim]),
name='high_decb');
y=tf.nn.sigmoid(tf.matmul(z,high_decW)+high_decb);
cost=tf.nn.l2_loss(x-y);
loss_per_pixel=tf.reduce_mean(tf.abs(x-y));
return {'x':x,'z':z,'y':y,'cost':cost,
'weights':hidden_weights,
'encW':high_encW,'decW':high_decW,
'encb':high_encb,'decb':high_decb,
'ppx':loss_per_pixel
};
def testGridLSTMCell(self):
with self.test_session() as sess:
num_units = 8
state_size = num_units * 2
batch_size = 3
input_size = 4
feature_size = 2
frequency_skip = 1
num_shifts = (input_size - feature_size) / frequency_skip + 1
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([batch_size, input_size])
m = tf.zeros([batch_size, state_size*num_shifts])
output, state = tf.contrib.rnn.GridLSTMCell(
num_units=num_units, feature_size=feature_size,
frequency_skip=frequency_skip, forget_bias=1.0)(x, m)
sess.run([tf.initialize_all_variables()])
res = sess.run([output, state],
{x.name: np.array([[1., 1., 1., 1.],
[2., 2., 2., 2.],
[3., 3., 3., 3.]]),
m.name: 0.1 * np.ones((batch_size, state_size*(
num_shifts)))})
self.assertEqual(len(res), 2)
# The numbers in results were not calculated, this is mostly just a
# smoke test.
self.assertEqual(res[0].shape, (batch_size, num_units*num_shifts*2))
self.assertEqual(res[1].shape, (batch_size, state_size*num_shifts))
# Different inputs so different outputs and states
for i in range(1, batch_size):
self.assertTrue(
float(np.linalg.norm((res[0][0, :] - res[0][i, :]))) > 1e-6)
self.assertTrue(
float(np.linalg.norm((res[1][0, :] - res[1][i, :]))) > 1e-6)
def autoencoder(d,c=5,tied_weights=False):
'''
An autoencoder network with one hidden layer (containing the encoding),
and sigmoid activation functions.
Args:
d: dimension of input.
c: dimension of code.
tied_weights: True if w1^T=w2
Returns:
Dictionary containing input placeholder Tensor and loss Variable
Raises:
'''
inputs = tf.placeholder(tf.float32, shape=[None,d], name='input')
w1 = tf.Variable(tf.truncated_normal([d,c], stddev=1.0/math.sqrt(d)))
b1 = tf.Variable(tf.zeros([c]))
w2 = tf.Variable(tf.truncated_normal([c,d], stddev=1.0/math.sqrt(c)))
# TODO: Implement tied weights
b2 = tf.Variable(tf.zeros([d]))
code = tf.nn.sigmoid(tf.matmul(inputs, w1)+b1, name='encoding')
reconstruction = tf.nn.sigmoid(tf.matmul(code, w2)+b2, name='reconstruction')
loss = tf.reduce_mean(tf.square(reconstruction - inputs))
tf.scalar_summary('loss', loss)
return {'inputs': inputs, 'loss': loss}
def testBasicLSTMCell(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m = tf.zeros([1, 8])
g, out_m = tf.nn.rnn_cell.MultiRNNCell(
[tf.nn.rnn_cell.BasicLSTMCell(2)] * 2)(x, m)
sess.run([tf.initialize_all_variables()])
res = sess.run([g, out_m], {x.name: np.array([[1., 1.]]),
m.name: 0.1 * np.ones([1, 8])})
self.assertEqual(len(res), 2)
# The numbers in results were not calculated, this is just a smoke test.
self.assertAllClose(res[0], [[0.24024698, 0.24024698]])
expected_mem = np.array([[0.68967271, 0.68967271,
0.44848421, 0.44848421,
0.39897051, 0.39897051,
0.24024698, 0.24024698]])
self.assertAllClose(res[1], expected_mem)
with tf.variable_scope("other", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 3]) # Test BasicLSTMCell with input_size != num_units.
m = tf.zeros([1, 4])
g, out_m = tf.nn.rnn_cell.BasicLSTMCell(2, input_size=3)(x, m)
sess.run([tf.initialize_all_variables()])
res = sess.run([g, out_m], {x.name: np.array([[1., 1., 1.]]),
m.name: 0.1 * np.ones([1, 4])})
self.assertEqual(len(res), 2)
def testCoupledInputForgetGateLSTMCell(self):
with self.test_session() as sess:
num_units = 2
state_size = num_units * 2
batch_size = 3
input_size = 4
expected_output = np.array(
[[0.121753, 0.121753],
[0.103349, 0.103349],
[0.100178, 0.100178]],
dtype=np.float32)
expected_state = np.array(
[[0.137523, 0.137523, 0.121753, 0.121753],
[0.105450, 0.105450, 0.103349, 0.103349],
[0.100742, 0.100742, 0.100178, 0.100178]],
dtype=np.float32)
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([batch_size, input_size])
m = tf.zeros([batch_size, state_size])
output, state = tf.contrib.rnn.CoupledInputForgetGateLSTMCell(
num_units=num_units, forget_bias=1.0)(x, m)
sess.run([tf.initialize_all_variables()])
res = sess.run([output, state],
{x.name: np.array([[1., 1., 1., 1.],
[2., 2., 2., 2.],
[3., 3., 3., 3.]]),
m.name: 0.1 * np.ones((batch_size, state_size))})
# This is a smoke test: Only making sure expected values didn't change.
self.assertEqual(len(res), 2)
self.assertAllClose(res[0], expected_output)
self.assertAllClose(res[1], expected_state)
def testLSTMCell(self):
with self.test_session() as sess:
num_units = 8
num_proj = 6
state_size = num_units + num_proj
batch_size = 3
input_size = 2
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([batch_size, input_size])
m = tf.zeros([batch_size, state_size])
output, state = tf.nn.rnn_cell.LSTMCell(
num_units=num_units, input_size=input_size,
num_proj=num_proj, forget_bias=1.0)(x, m)
sess.run([tf.initialize_all_variables()])
res = sess.run([output, state],
{x.name: np.array([[1., 1.], [2., 2.], [3., 3.]]),
m.name: 0.1 * np.ones((batch_size, state_size))})
self.assertEqual(len(res), 2)
# The numbers in results were not calculated, this is mostly just a
# smoke test.
self.assertEqual(res[0].shape, (batch_size, num_proj))
self.assertEqual(res[1].shape, (batch_size, state_size))
# Different inputs so different outputs and states
for i in range(1, batch_size):
self.assertTrue(
float(np.linalg.norm((res[0][0, :] - res[0][i, :]))) > 1e-6)
self.assertTrue(
float(np.linalg.norm((res[1][0, :] - res[1][i, :]))) > 1e-6)
def inference(images,hidden1_units,hidden2_units):
"""建立前馈神经网络模型
Args:
images:输入图像数据
hidden1_units:第一个隐藏层的神经元数目
hidden2_units:第二个隐藏层 的神经元数目
returns:
softmax_linear:输出张量为计算后的结果
"""
#隐藏层1
with tf.name_scope('hidden1'):
weights = tf.Variable(tf.truncated_normal([IMAGE_PIXELS,hidden1_units],stddev=1.0/math.sqrt(float(IMAGE_PIXELS))),name='weights')#?
biases = tf.Variable(tf.zeros([hidden1_units]),name='biases')
hidden1 = tf.nn.relu(tf.matmul(images,weights)+biases)
#隐藏层2
with tf.name_scope('hidden2'):
weights = tf.Variable(tf.truncated_normal([hidden1_units,hidden2_units],stddev=1.0/math.sqrt(float(hidden1_units))),name='weights')
biases = tf.Variable(tf.zeros([hidden2_units]),name='biases')
hidden2 = tf.nn.relu(tf.matmul(hidden1,weights)+biases)
#线性输出层
with tf.name_scope('softmax_linear'):
weights = tf.Variable(tf.truncated_normal([hidden2_units,NUM_CLASSES]),name='biases')
biases = tf.Variable(tf.zeros([NUM_CLASSES]),name='biases')
logits = tf.matmul(hidden2,weights) + biases
return logits
def testBasicLSTMCellStateTupleType(self):
with self.test_session():
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m0 = (tf.zeros([1, 2]),) * 2
m1 = (tf.zeros([1, 2]),) * 2
cell = tf.nn.rnn_cell.MultiRNNCell(
[tf.nn.rnn_cell.BasicLSTMCell(2)] * 2,
state_is_tuple=True)
self.assertTrue(isinstance(cell.state_size, tuple))
self.assertTrue(isinstance(cell.state_size[0],
tf.nn.rnn_cell.LSTMStateTuple))
self.assertTrue(isinstance(cell.state_size[1],
tf.nn.rnn_cell.LSTMStateTuple))
# Pass in regular tuples
_, (out_m0, out_m1) = cell(x, (m0, m1))
self.assertTrue(isinstance(out_m0,
tf.nn.rnn_cell.LSTMStateTuple))
self.assertTrue(isinstance(out_m1,
tf.nn.rnn_cell.LSTMStateTuple))
# Pass in LSTMStateTuples
tf.get_variable_scope().reuse_variables()
zero_state = cell.zero_state(1, tf.float32)
self.assertTrue(isinstance(zero_state, tuple))
self.assertTrue(isinstance(zero_state[0],
tf.nn.rnn_cell.LSTMStateTuple))
self.assertTrue(isinstance(zero_state[1],
tf.nn.rnn_cell.LSTMStateTuple))
_, (out_m0, out_m1) = cell(x, zero_state)
self.assertTrue(
isinstance(out_m0, tf.nn.rnn_cell.LSTMStateTuple))
self.assertTrue(
isinstance(out_m1, tf.nn.rnn_cell.LSTMStateTuple))
def __init__(self, name, input_size, output_size):
with tf.name_scope("rbm_" + name):
self.weights = tf.Variable(
tf.truncated_normal([input_size, output_size],
stddev=1.0 / math.sqrt(float(input_size))), name="weights")
self.v_bias = tf.Variable(tf.zeros([input_size]), name="v_bias")
self.h_bias = tf.Variable(tf.zeros([output_size]), name="h_bias")
def model(images, inits, num_iterations=4, num_patches=68, patch_shape=(24, 24), num_channels=3):
batch_size = images.get_shape().as_list()[0]
hidden_state = tf.zeros((batch_size, 512))
dx = tf.zeros((batch_size, num_patches, 2))
endpoints = {}
dxs = []
for step in range(num_iterations):
with tf.device('/cpu:0'):
patches = tf.image.extract_patches(images, tf.constant(patch_shape), inits+dx)
patches = tf.reshape(patches, (batch_size * num_patches, patch_shape[0], patch_shape[1], num_channels))
endpoints['patches'] = patches
with tf.variable_scope('convnet', reuse=step>0):
net = conv_model(patches)
ims = net['concat']
ims = tf.reshape(ims, (batch_size, -1))
with tf.variable_scope('rnn', reuse=step>0) as scope:
hidden_state = slim.ops.fc(tf.concat(1, [ims, hidden_state]), 512, activation=tf.tanh)
prediction = slim.ops.fc(hidden_state, num_patches * 2, scope='pred', activation=None)
endpoints['prediction'] = prediction
prediction = tf.reshape(prediction, (batch_size, num_patches, 2))
dx += prediction
dxs.append(dx)
return inits + dx, dxs, endpoints
def testGradientsAsVariables(self):
for dtype in [tf.half, tf.float32, tf.float64]:
with self.test_session() as sess:
var0 = tf.Variable([1.0, 2.0], dtype=dtype)
var1 = tf.Variable([3.0, 4.0], dtype=dtype)
cost = 5 * var0 + 3 * var1
global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
sgd_op = tf.train.GradientDescentOptimizer(3.0)
grads_and_vars = sgd_op.compute_gradients(cost, [var0, var1])
# Convert gradients to tf.Variables
converted_grads = [
tf.Variable(tf.zeros([2], dtype)) for i in grads_and_vars
]
convert_ops = [
tf.assign(converted_grads[i], gv[0])
for i, gv in enumerate(grads_and_vars)
]
converted_grads_and_vars = list(zip(converted_grads, [var0, var1]))
opt_op = sgd_op.apply_gradients(converted_grads_and_vars, global_step)
tf.global_variables_initializer().run()
# Run convert_ops to achieve the gradietns converting
sess.run(convert_ops)
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
# Run 1 step of sgd through optimizer
opt_op.run()
# Validate updated params
self.assertAllClose([-14., -13.], var0.eval())
self.assertAllClose([-6., -5.], var1.eval())
def testBasicLSTMCellWithStateTuple(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m0 = tf.zeros([1, 4])
m1 = tf.zeros([1, 4])
cell = tf.nn.rnn_cell.MultiRNNCell(
[tf.nn.rnn_cell.BasicLSTMCell(2)] * 2, state_is_tuple=True)
g, (out_m0, out_m1) = cell(x, (m0, m1))
sess.run([tf.initialize_all_variables()])
res = sess.run([g, out_m0, out_m1],
{x.name: np.array([[1., 1.]]),
m0.name: 0.1 * np.ones([1, 4]),
m1.name: 0.1 * np.ones([1, 4])})
self.assertEqual(len(res), 3)
# The numbers in results were not calculated, this is just a smoke test.
# Note, however, these values should match the original
# version having state_is_tuple=False.
self.assertAllClose(res[0], [[0.24024698, 0.24024698]])
expected_mem0 = np.array([[0.68967271, 0.68967271,
0.44848421, 0.44848421]])
expected_mem1 = np.array([[0.39897051, 0.39897051,
0.24024698, 0.24024698]])
self.assertAllClose(res[1], expected_mem0)
self.assertAllClose(res[2], expected_mem1)
def testCompatibleNames(self):
with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()):
cell = tf.nn.rnn_cell.LSTMCell(10)
pcell = tf.nn.rnn_cell.LSTMCell(10, use_peepholes=True)
inputs = [tf.zeros([4, 5])] * 6
tf.nn.rnn(cell, inputs, dtype=tf.float32, scope="basic")
tf.nn.rnn(pcell, inputs, dtype=tf.float32, scope="peephole")
basic_names = {v.name: v.get_shape() for v in tf.trainable_variables()}
with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()):
cell = tf.contrib.rnn.LSTMBlockCell(10, use_compatible_names=True)
pcell = tf.contrib.rnn.LSTMBlockCell(
10, use_peephole=True, use_compatible_names=True)
inputs = [tf.zeros([4, 5])] * 6
tf.nn.rnn(cell, inputs, dtype=tf.float32, scope="basic")
tf.nn.rnn(pcell, inputs, dtype=tf.float32, scope="peephole")
block_names = {v.name: v.get_shape() for v in tf.trainable_variables()}
with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()):
cell = tf.contrib.rnn.LSTMBlockFusedCell(10)
pcell = tf.contrib.rnn.LSTMBlockFusedCell(10, use_peephole=True)
inputs = [tf.zeros([4, 5])] * 6
cell(inputs, dtype=tf.float32, scope="basic/LSTMCell")
pcell(inputs, dtype=tf.float32, scope="peephole/LSTMCell")
fused_names = {v.name: v.get_shape() for v in tf.trainable_variables()}
self.assertEqual(basic_names, block_names)
self.assertEqual(basic_names, fused_names)
def testMultiRNNCellWithStateTuple(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m_bad = tf.zeros([1, 4])
m_good = (tf.zeros([1, 2]), tf.zeros([1, 2]))
# Test incorrectness of state
with self.assertRaisesRegexp(ValueError, "Expected state .* a tuple"):
tf.nn.rnn_cell.MultiRNNCell(
[tf.nn.rnn_cell.GRUCell(2)] * 2, state_is_tuple=True)(x, m_bad)
_, ml = tf.nn.rnn_cell.MultiRNNCell(
[tf.nn.rnn_cell.GRUCell(2)] * 2, state_is_tuple=True)(x, m_good)
sess.run([tf.initialize_all_variables()])
res = sess.run(ml, {x.name: np.array([[1., 1.]]),
m_good[0].name: np.array([[0.1, 0.1]]),
m_good[1].name: np.array([[0.1, 0.1]])})
# The numbers in results were not calculated, this is just a
# smoke test. However, these numbers should match those of
# the test testMultiRNNCell.
self.assertAllClose(res[0], [[0.175991, 0.175991]])
self.assertAllClose(res[1], [[0.13248, 0.13248]])
def embedding(inputs,
vocab_size,
num_units,
zero_pad=True,
scale=True,
l2_reg=0.0,
scope="embedding",
with_t=False,
reuse=None):
'''Embeds a given tensor.
Args:
inputs: A `Tensor` with type `int32` or `int64` containing the ids
to be looked up in `lookup table`.
vocab_size: An int. Vocabulary size.
num_units: An int. Number of embedding hidden units.
zero_pad: A boolean. If True, all the values of the fist row (id 0)
should be constant zeros.
scale: A boolean. If True. the outputs is multiplied by sqrt num_units.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A `Tensor` with one more rank than inputs's. The last dimensionality
should be `num_units`.
For example,
```
import tensorflow as tf
inputs = tf.to_int32(tf.reshape(tf.range(2*3), (2, 3)))
outputs = embedding(inputs, 6, 2, zero_pad=True)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print sess.run(outputs)
>>
[[[ 0. 0. ]
[ 0.09754146 0.67385566]
[ 0.37864095 -0.35689294]]
[[-1.01329422 -1.09939694]
[ 0.7521342 0.38203377]
[-0.04973143 -0.06210355]]]
```
```
import tensorflow as tf
inputs = tf.to_int32(tf.reshape(tf.range(2*3), (2, 3)))
outputs = embedding(inputs, 6, 2, zero_pad=False)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print sess.run(outputs)
>>
[[[-0.19172323 -0.39159766]
[-0.43212751 -0.66207761]
[ 1.03452027 -0.26704335]]
[[-0.11634696 -0.35983452]
[ 0.50208133 0.53509563]
[ 1.22204471 -0.96587461]]]
```
'''
with tf.variable_scope(scope, reuse=reuse):
lookup_table = tf.get_variable('lookup_table',
dtype=tf.float32,
shape=[vocab_size, num_units],
#initializer=tf.contrib.layers.xavier_initializer(),
regularizer=tf.contrib.layers.l2_regularizer(l2_reg))
if zero_pad:
lookup_table = tf.concat((tf.zeros(shape=[1, num_units]),
lookup_table[1:, :]), 0)
outputs = tf.nn.embedding_lookup(lookup_table, inputs)
if scale:
outputs = outputs * (num_units ** 0.5)
if with_t: return outputs,lookup_table
else: return outputs
u = tf.cast(e[0][0], tf.int32)
v = tf.cast(e[0][1], tf.int32)
y_t = []
h = tf.placeholder(dtype = tf.float32, shape = [1, 10])
for i in range(84):
temp1 = tf.gather_nd(adj, (i, u))
temp2 = tf.gather(y, u)
temp3 = tf.gather_nd(adj, (i,v))
temp4 = tf.gather(y, v)
temp5 = tf.multiply(temp1, temp2)
temp6 = tf.multiply(temp3, temp4)
intermediate = tf.add_n([temp5, temp6])
# print "Debug intermediate size", intermediate.get_shape(), h_inter.get_shape()
# intermediate = tf.add_n(tf.multiply(tf.gather_nd(self.adj, (i, u)), tf.gather(y, u)), tf.multiply(tf.gather_nd(self.adj, (i,v)), tf.gather(y, v)))
first = tf.concat([[y[i]], [intermediate], h], axis=1)
second = tf.concat([tf.zeros([1, 2 * 1]), h], axis=1)
y_t.append(tf.concat([tf.add(first, second), b], axis = 1))
c = tf.stack(y_t)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
#h = tf.placeholder(dtype = tf.float32, shape = [1])
# Runs the op.
summary_writer = tf.summary.FileWriter('logs/' + datetime.now().isoformat().replace(':', '-'), sess.graph)
print("len", len(tf.get_default_graph().get_operations()))
t1 = time.time()
print(sess.run([c], feed_dict = {y:np.ones([84, 1]), b:np.zeros([1, 5]), h:np.ones([1, 10]), e:[[1,2,0,0]], adj:np.ones([84, 84])}))
t2 = time.time()
def __init__(self,
data_dir,
embed_dim=100,
combination_method='simple',
dropout=0.5,
neg_weight=0.5):
if combination_method.lower() not in ['simple', 'matrix']:
raise NotImplementedError(
"ProjE does not support using %s as combination method." %
combination_method)
self.__combination_method = combination_method
self.__embed_dim = embed_dim
self.__initialized = False
self.__trainable = list()
self.__dropout = dropout
# with codecs.open(os.path.join(data_dir, 'entity2id.txt'), 'r', encoding='utf-8') as f:
# self.__n_entity = len(f.readlines())
# with codecs.open(os.path.join(data_dir, 'entity2id.txt'), 'r', encoding='utf-8') as f:
# self.__entity_id_map = {x.strip().split('\t')[0]: int(x.strip().split('\t')[1]) for x in f.readlines()}
# self.__id_entity_map = {v: k for k, v in self.__entity_id_map.items()}
wikidata, reverse_dict, item_data, prop_data, wikidata_fanout_dict, child_par_dict = load_wikidata(
)
print len(wikidata)
# global wikidata
# wikidata_key_sample = random.sample(wikidata.keys(),50000)
# wikidata = dict((k, wikidata[k]) for k in wikidata_key_sample)
self.__n_entity = len(wikidata)
print("N_ENTITY: %d" % self.__n_entity)
# with codecs.open(os.path.join(data_dir, 'relation2id.txt'), 'r', encoding='utf-8') as f:
# self.__n_relation = len(f.readlines())
self.__n_relation = len(prop_data)
# with codecs.open(os.path.join(data_dir, 'relation2id.txt'), 'r', encoding='utf-8') as f:
# self.__relation_id_map = {x.strip().split('\t')[0]: int(x.strip().split('\t')[1]) for x in f.readlines()}
# self.__id_relation_map = {v: k for k, v in self.__entity_id_map.items()}
self.__relation_id_map = {
pid: i
for i, pid in enumerate(prop_data.keys())
}
self.__entity_id_map = {
qid: i
for i, qid in enumerate(wikidata.keys())
}
self.__id_relation_map = {
i: pid
for i, pid in enumerate(prop_data.keys())
}
self.__id_entity_map = {
i: qid
for i, qid in enumerate(wikidata.keys())
}
print("N_RELATION: %d" % self.__n_relation)
# def load_triple(file_path):
# with codecs.open(file_path, 'r', encoding='utf-8') as f_triple:
# return np.asarray([[self.__entity_id_map[x.strip().split('\t')[0]],
# self.__entity_id_map[x.strip().split('\t')[1]],
# self.__relation_id_map[x.strip().split('\t')[2]]] for x in f_triple.readlines()],
# dtype=np.int32)
def load_triple():
triples_arr = []
for QID in wikidata:
for pid in [p for p in wikidata[QID] if p in prop_data]:
for qid in [
q for q in wikidata[QID][pid] if
q in child_par_dict and q in self.__entity_id_map
]:
triples_arr.append([
self.__entity_id_map[QID],
self.__entity_id_map[qid],
self.__relation_id_map[pid]
])
if len(triples_arr) > 100000:
return np.asarray(triples_arr)
return np.asarray(triples_arr)
def gen_hr_t(triple_data):
hr_t = dict()
for h, t, r in triple_data:
if h not in hr_t:
hr_t[h] = dict()
if r not in hr_t[h]:
hr_t[h][r] = set()
hr_t[h][r].add(t)
return hr_t
def gen_tr_h(triple_data):
tr_h = dict()
for h, t, r in triple_data:
if t not in tr_h:
tr_h[t] = dict()
if r not in tr_h[t]:
tr_h[t][r] = set()
tr_h[t][r].add(h)
return tr_h
triples_arr = load_triple()
idx = np.random.permutation(np.arange(triples_arr.shape[0]))
self.__train_triple = triples_arr[:int(0.7 * idx.shape[0])]
self.__valid_triple = triples_arr[int(0.7 *
idx.shape[0]):int(0.8 *
idx.shape[0])]
self.__test_triple = triples_arr[int(0.8 * idx.shape[0]):]
# self.__train_triple = load_triple(os.path.join(data_dir, 'train.txt'))
print("N_TRAIN_TRIPLES: %d" % self.__train_triple.shape[0])
# self.__test_triple = load_triple(os.path.join(data_dir, 'test.txt'))
print("N_TEST_TRIPLES: %d" % self.__test_triple.shape[0])
# self.__valid_triple = load_triple(os.path.join(data_dir, 'valid.txt'))
print("N_VALID_TRIPLES: %d" % self.__valid_triple.shape[0])
self.__train_hr_t = gen_hr_t(self.__train_triple)
self.__train_tr_h = gen_tr_h(self.__train_triple)
self.__test_hr_t = gen_hr_t(self.__test_triple)
self.__test_tr_h = gen_tr_h(self.__test_triple)
print 'flag 1'
self.__hr_t = gen_hr_t(
np.concatenate(
[self.__train_triple, self.__test_triple, self.__valid_triple],
axis=0))
print 'flag 2'
self.__tr_h = gen_tr_h(
np.concatenate(
[self.__train_triple, self.__test_triple, self.__valid_triple],
axis=0))
print 'flag 3'
bound = 6 / math.sqrt(embed_dim)
with tf.device('/cpu'):
self.__ent_embedding = tf.get_variable(
"ent_embedding", [self.__n_entity, embed_dim],
initializer=tf.random_uniform_initializer(minval=-bound,
maxval=bound,
seed=345))
self.__trainable.append(self.__ent_embedding)
self.__rel_embedding = tf.get_variable(
"rel_embedding", [self.__n_relation, embed_dim],
initializer=tf.random_uniform_initializer(minval=-bound,
maxval=bound,
seed=346))
self.__trainable.append(self.__rel_embedding)
print 'flag 4'
if combination_method.lower() == 'simple':
self.__hr_weighted_vector = tf.get_variable(
"simple_hr_combination_weights", [embed_dim * 2],
initializer=tf.random_uniform_initializer(minval=-bound,
maxval=bound,
seed=445))
print 'flag 5'
self.__tr_weighted_vector = tf.get_variable(
"simple_tr_combination_weights", [embed_dim * 2],
initializer=tf.random_uniform_initializer(minval=-bound,
maxval=bound,
seed=445))
print 'flag 6'
self.__trainable.append(self.__hr_weighted_vector)
self.__trainable.append(self.__tr_weighted_vector)
self.__hr_combination_bias = tf.get_variable(
"combination_bias_hr", initializer=tf.zeros([embed_dim]))
self.__tr_combination_bias = tf.get_variable(
"combination_bias_tr", initializer=tf.zeros([embed_dim]))
print 'flag 7'
self.__trainable.append(self.__hr_combination_bias)
self.__trainable.append(self.__tr_combination_bias)
else:
self.__hr_combination_matrix = tf.get_variable(
"matrix_hr_combination_layer", [embed_dim * 2, embed_dim],
initializer=tf.random_uniform_initializer(minval=-bound,
maxval=bound,
seed=555))
self.__tr_combination_matrix = tf.get_variable(
"matrix_tr_combination_layer", [embed_dim * 2, embed_dim],
initializer=tf.random_uniform_initializer(minval=-bound,
maxval=bound,
seed=555))
self.__trainable.append(self.__hr_combination_matrix)
self.__trainable.append(self.__tr_combination_matrix)
self.__hr_combination_bias = tf.get_variable(
"combination_bias_hr", initializer=tf.zeros([embed_dim]))
self.__tr_combination_bias = tf.get_variable(
"combination_bias_tr", initializer=tf.zeros([embed_dim]))
self.__trainable.append(self.__hr_combination_bias)
self.__trainable.append(self.__tr_combination_bias)
# DONOTCHANGE: They are reserved for nsml
args.add_argument('--mode', type=str, default='train', help='submit일때 해당값이 test로 설정됩니다.')
args.add_argument('--iteration', type=str, default='0',
help='fork 명령어를 입력할때의 체크포인트로 설정됩니다. 체크포인트 옵션을 안주면 마지막 wall time 의 model 을 가져옵니다.')
args.add_argument('--pause', type=int, default=0, help='model 을 load 할때 1로 설정됩니다.')
config = args.parse_args()
with tf.Graph().as_default():
# placeholder is used for feeding data.
x = tf.placeholder("float", shape=[None, FEATURE_DIM]) # none represents variable length of dimension.
y_target = tf.placeholder("float", shape=[None, OUTPUT_DIM]) # shape argument is optional, but this is useful to debug.
W1 = tf.Variable(tf.zeros([FEATURE_DIM, OUTPUT_DIM]))
b1 = tf.Variable(tf.zeros([OUTPUT_DIM]))
y = tf.matmul(x, W1) + b1
delta = (y - y_target)
L1_delta = delta[:, 0]
L2_delta = delta[:, 1]
MSE_loss = tf.reduce_mean(0.3* L1_delta*L1_delta + 0.7*L2_delta*L2_delta)
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(MSE_loss)
sess = tf.Session() # open a session which is a envrionment of computation graph.
sess.run(tf.global_variables_initializer())# initialize the variables
# Bind model
run_params = [y, x]
def __init__(self, root_embeddings: tf.Tensor, tree_def: TreeDefinition, target_trees: T.List[Tree] = None):
super(self.__class__, self).__init__()
self.is_supervised = target_trees is not None
self.root_embeddings = root_embeddings
self.target_trees = target_trees
self.decoded_trees = [] # populated by the decoder
self.tree_def = tree_def
self.deferred_value_types = {}
for nt in tree_def.node_types:
if nt.value_type is not None:
self.counters['vals_' + nt.id] = 1
self.deferred_value_types[nt.id] = self.BuildDeferredRepresentationValueType(nt)
# supervised call
if self.is_supervised:
self.depths = {'embs': []}
def init(n: Tree, depth=0):
nonlocal self
n.meta['dec_batch'] = self
n.meta['node_numb'] = self.counters['embs']
self.counters['embs'] += 1
self.depths['embs'].append(depth)
if n.value is not None:
k = 'vals_' + n.node_type_id
n.meta['value_numb'] = self.counters[k]
if k not in self.depths.keys():
self.depths[k] = []
self.depths[k].append(depth)
self.counters[k] += 1
self.map_to_all_nodes(init, target_trees)
self['embs'] = tf.zeros([self.counters['embs'] + 1, root_embeddings.shape[1]])
# save the initial embeddings in the store se we can easily gather them afterwards
self.scatter_update('embs', [t.meta['node_numb'] for t in target_trees], root_embeddings)
_depths = {k: tf.convert_to_tensor(self.depths[k], dtype=tf.float32) for k in self.depths.keys()}
self.depths = _depths
self.max_depths = {k: tf.reduce_max(self.depths[k]) for k in self.depths.keys()}
scale_start, scale_end, scale_exp, max_depth = 1.0, 0.0, 2.0, 70
self.depth_scale_coefs = {
k: scale_start + (scale_end - scale_start) * (self.depths[k] / max_depth) ** scale_exp for k in self.depths.keys()
}
# don't need to index them, never gather nor rewrite them - only used altoghether for the loss.
# thus incrementally stack-up into a constant
# root distrib is saved with some zero padding
# more over some are not associated to a real node, artificial nodes used to train the model
# to -not- generate a node (special no-child/no-node)
self.distribs_unscaled = []
self.distribs_idx = []
for nt in tree_def.node_types:
if nt.value_type is not None:
self['vals_'+nt.id] = tf.zeros([self.counters['vals_'+nt.id] + 1,nt.value_type.representation_shape])
# unsupervised call
else:
self['embs'] = tf.zeros([1, root_embeddings.shape[1]])
for nt in tree_def.node_types:
if nt.value_type is not None:
self['vals_'+nt.id] = tf.zeros([1, nt.value_type.representation_shape])
# -*- coding: utf-8 -*-
import tensorflow as tf
sess = tf.Session()
x = tf.placeholder("float", shape=[None, 10], name="input")
W = tf.Variable(tf.truncated_normal([10, 5], stddev=0.05))
b = tf.Variable(tf.zeros([5]))
y = tf.nn.softmax(tf.matmul(x, W) + b, name="output")
sess.run(tf.initialize_all_variables())
def main(argv=None): # pylint: disable=unused-argument
data_dir = './training/training/'
train_data_filename = data_dir + 'images/'
train_labels_filename = data_dir + 'groundtruth/'
# Extract it into numpy arrays.
train_data = extract_data(train_data_filename, TRAINING_SIZE)
train_labels = extract_labels(train_labels_filename, TRAINING_SIZE)
num_epochs = NUM_EPOCHS
c0 = 0
c1 = 0
for i in range(len(train_labels)):
if train_labels[i][0] == 1:
c0 = c0 + 1
else:
c1 = c1 + 1
print('Number of data points per class: c0 = ' + str(c0) + ' c1 = ' +
str(c1))
print('Balancing training data...')
min_c = min(c0, c1)
idx0 = [i for i, j in enumerate(train_labels) if j[0] == 1]
idx1 = [i for i, j in enumerate(train_labels) if j[1] == 1]
new_indices = idx0[0:min_c] + idx1[0:min_c]
print(len(new_indices))
print(train_data.shape)
train_data = train_data[new_indices, :, :, :]
train_labels = train_labels[new_indices]
train_size = train_labels.shape[0]
c0 = 0
c1 = 0
for i in range(len(train_labels)):
if train_labels[i][0] == 1:
c0 = c0 + 1
else:
c1 = c1 + 1
print('Number of data points per class: c0 = ' + str(c0) + ' c1 = ' +
str(c1))
# This is where training samples and labels are fed to the graph.
# These placeholder nodes will be fed a batch of training data at each
# training step using the {feed_dict} argument to the Run() call below.
train_data_node = tf.placeholder(tf.float32,
shape=(BATCH_SIZE, IMG_PATCH_SIZE,
IMG_PATCH_SIZE, NUM_CHANNELS))
train_labels_node = tf.placeholder(tf.float32,
shape=(BATCH_SIZE, NUM_LABELS))
train_all_data_node = tf.constant(train_data)
# The variables below hold all the trainable weights. They are passed an
# initial value which will be assigned when when we call:
# {tf.initialize_all_variables().run()}
conv1_weights = tf.Variable(
tf.truncated_normal(
[5, 5, NUM_CHANNELS, 32], # 5x5 filter, depth 32.
stddev=0.1,
seed=SEED))
conv1_biases = tf.Variable(tf.zeros([32]))
conv2_weights = tf.Variable(
tf.truncated_normal([5, 5, 32, 64], stddev=0.1, seed=SEED))
conv2_biases = tf.Variable(tf.constant(0.1, shape=[64]))
fc1_weights = tf.Variable( # fully connected, depth 512.
tf.truncated_normal(
[int(IMG_PATCH_SIZE / 4 * IMG_PATCH_SIZE / 4 * 64), 512],
stddev=0.1,
seed=SEED))
fc1_biases = tf.Variable(tf.constant(0.1, shape=[512]))
fc2_weights = tf.Variable(
tf.truncated_normal([512, NUM_LABELS], stddev=0.1, seed=SEED))
fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS]))
# Make an image summary for 4d tensor image with index idx
def get_image_summary(img, idx=0):
V = tf.slice(img, (0, 0, 0, idx), (1, -1, -1, 1))
img_w = img.get_shape().as_list()[1]
img_h = img.get_shape().as_list()[2]
min_value = tf.reduce_min(V)
V = V - min_value
max_value = tf.reduce_max(V)
V = V / (max_value * PIXEL_DEPTH)
V = tf.reshape(V, (img_w, img_h, 1))
V = tf.transpose(V, (2, 0, 1))
V = tf.reshape(V, (-1, img_w, img_h, 1))
return V
# Make an image summary for 3d tensor image with index idx
def get_image_summary_3d(img):
V = tf.slice(img, (0, 0, 0), (1, -1, -1))
img_w = img.get_shape().as_list()[1]
img_h = img.get_shape().as_list()[2]
V = tf.reshape(V, (img_w, img_h, 1))
V = tf.transpose(V, (2, 0, 1))
V = tf.reshape(V, (-1, img_w, img_h, 1))
return V
# Get prediction for given input image
def get_prediction(img):
data = numpy.asarray(img_crop(img, IMG_PATCH_SIZE, IMG_PATCH_SIZE))
data_node = tf.constant(data)
output = tf.nn.softmax(model(data_node))
output_prediction = s.run(output)
img_prediction = label_to_img(img.shape[0], img.shape[1],
IMG_PATCH_SIZE, IMG_PATCH_SIZE,
output_prediction)
return img_prediction
# Get a concatenation of the prediction and groundtruth for given input file
def get_prediction_with_groundtruth(filename, image_idx):
imageid = "satImage_%.3d" % image_idx
image_filename = filename + imageid + ".png"
img = mpimg.imread(image_filename)
img_prediction = get_prediction(img)
cimg = concatenate_images(img, img_prediction)
return cimg
# Get prediction overlaid on the original image for given input file
def get_prediction_with_overlay(filename, image_idx):
imageid = "satImage_%.3d" % image_idx
image_filename = filename + imageid + ".png"
img = mpimg.imread(image_filename)
img_prediction = get_prediction(img)
oimg = make_img_overlay(img, img_prediction)
return oimg
# We will replicate the model structure for the training subgraph, as well
# as the evaluation subgraphs, while sharing the trainable parameters.
def model(data, train=False):
"""The Model definition."""
# 2D convolution, with 'SAME' padding (i.e. the output feature map has
# the same size as the input). Note that {strides} is a 4D array whose
# shape matches the data layout: [image index, y, x, depth].
conv = tf.nn.conv2d(data,
conv1_weights,
strides=[1, 1, 1, 1],
padding='SAME')
# Bias and rectified linear non-linearity.
relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))
# Max pooling. The kernel size spec {ksize} also follows the layout of
# the data. Here we have a pooling window of 2, and a stride of 2.
pool = tf.nn.max_pool(relu,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
conv2 = tf.nn.conv2d(pool,
conv2_weights,
strides=[1, 1, 1, 1],
padding='SAME')
relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))
pool2 = tf.nn.max_pool(relu2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
# Uncomment these lines to check the size of each layer
# print 'data ' + str(data.get_shape())
# print 'conv ' + str(conv.get_shape())
# print 'relu ' + str(relu.get_shape())
# print 'pool ' + str(pool.get_shape())
# print 'pool2 ' + str(pool2.get_shape())
# Reshape the feature map cuboid into a 2D matrix to feed it to the
# fully connected layers.
pool_shape = pool2.get_shape().as_list()
reshape = tf.reshape(
pool2,
[pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])
# Fully connected layer. Note that the '+' operation automatically
# broadcasts the biases.
hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)
# Add a 50% dropout during training only. Dropout also scales
# activations such that no rescaling is needed at evaluation time.
#if train:
# hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)
out = tf.matmul(hidden, fc2_weights) + fc2_biases
if train == True:
summary_id = '_0'
s_data = get_image_summary(data)
filter_summary0 = tf.summary.image('summary_data' + summary_id,
s_data)
s_conv = get_image_summary(conv)
filter_summary2 = tf.summary.image('summary_conv' + summary_id,
s_conv)
s_pool = get_image_summary(pool)
filter_summary3 = tf.summary.image('summary_pool' + summary_id,
s_pool)
s_conv2 = get_image_summary(conv2)
filter_summary4 = tf.summary.image('summary_conv2' + summary_id,
s_conv2)
s_pool2 = get_image_summary(pool2)
filter_summary5 = tf.summary.image('summary_pool2' + summary_id,
s_pool2)
return out
# Training computation: logits + cross-entropy loss.
logits = model(train_data_node, True) # BATCH_SIZE*NUM_LABELS
# print 'logits = ' + str(logits.get_shape()) + ' train_labels_node = ' + str(train_labels_node.get_shape())
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits,
labels=train_labels_node))
tf.summary.scalar('loss', loss)
all_params_node = [
conv1_weights, conv1_biases, conv2_weights, conv2_biases, fc1_weights,
fc1_biases, fc2_weights, fc2_biases
]
all_params_names = [
'conv1_weights', 'conv1_biases', 'conv2_weights', 'conv2_biases',
'fc1_weights', 'fc1_biases', 'fc2_weights', 'fc2_biases'
]
all_grads_node = tf.gradients(loss, all_params_node)
all_grad_norms_node = []
for i in range(0, len(all_grads_node)):
norm_grad_i = tf.global_norm([all_grads_node[i]])
all_grad_norms_node.append(norm_grad_i)
tf.summary.scalar(all_params_names[i], norm_grad_i)
# L2 regularization for the fully connected parameters.
regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +
tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))
# Add the regularization term to the loss.
loss += 5e-4 * regularizers
# Optimizer: set up a variable that's incremented once per batch and
# controls the learning rate decay.
batch = tf.Variable(0)
# Decay once per epoch, using an exponential schedule starting at 0.01.
learning_rate = tf.train.exponential_decay(
0.01, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
train_size, # Decay step.
0.95, # Decay rate.
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
# Use simple momentum for the optimization.
optimizer = tf.train.MomentumOptimizer(learning_rate,
0.0).minimize(loss,
global_step=batch)
# Predictions for the minibatch, validation set and test set.
train_prediction = tf.nn.softmax(logits)
# We'll compute them only once in a while by calling their {eval()} method.
train_all_prediction = tf.nn.softmax(model(train_all_data_node))
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Create a local session to run this computation.
with tf.Session() as s:
if RESTORE_MODEL:
# Restore variables from disk.
saver.restore(s, FLAGS.train_dir + "/model.ckpt")
print("Model restored.")
else:
# Run all the initializers to prepare the trainable parameters.
tf.initialize_all_variables().run()
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.train_dir,
graph_def=s.graph_def)
print('Initialized!')
# Loop through training steps.
print('Total number of iterations = ' +
str(int(num_epochs * train_size / BATCH_SIZE)))
training_indices = range(train_size)
for iepoch in range(num_epochs):
# Permute training indices
perm_indices = numpy.random.permutation(training_indices)
for step in range(int(train_size / BATCH_SIZE)):
offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)
batch_indices = perm_indices[offset:(offset + BATCH_SIZE)]
# Compute the offset of the current minibatch in the data.
# Note that we could use better randomization across epochs.
batch_data = train_data[batch_indices, :, :, :]
batch_labels = train_labels[batch_indices]
# This dictionary maps the batch data (as a numpy array) to the
# node in the graph is should be fed to.
feed_dict = {
train_data_node: batch_data,
train_labels_node: batch_labels
}
if step % RECORDING_STEP == 0:
summary_str, _, l, lr, predictions = s.run(
[
summary_op, optimizer, loss, learning_rate,
train_prediction
],
feed_dict=feed_dict)
#summary_str = s.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
# print_predictions(predictions, batch_labels)
print('Epoch %.2f' %
(float(step) * BATCH_SIZE / train_size))
print('Minibatch loss: %.3f, learning rate: %.6f' %
(l, lr))
print('Minibatch error: %.1f%%' %
error_rate(predictions, batch_labels))
sys.stdout.flush()
else:
# Run the graph and fetch some of the nodes.
_, l, lr, predictions = s.run(
[optimizer, loss, learning_rate, train_prediction],
feed_dict=feed_dict)
# Save the variables to disk.
save_path = saver.save(s, FLAGS.train_dir + "/model.ckpt")
print("Model saved in file: %s" % save_path)
print("Running prediction on training set")
prediction_training_dir = "predictions_training/"
if not os.path.isdir(prediction_training_dir):
os.mkdir(prediction_training_dir)
for i in range(1, TRAINING_SIZE + 1):
pimg = get_prediction_with_groundtruth(train_data_filename, i)
Image.fromarray(pimg).save(prediction_training_dir +
"prediction_" + str(i) + ".png")
oimg = get_prediction_with_overlay(train_data_filename, i)
oimg.save(prediction_training_dir + "overlay_" + str(i) + ".png")
mnist = input_data.read_data_sets('mnist_data', one_hot = True)
# size of batch
batch_size = 100
#all size
n_batch = mnist.train.num_examples // batch_size
#define two placeholder
x = tf.placeholder(tf.float32,[None, 784])
y = tf.placeholder(tf.float32,[None, 10])
lr = tf.Variable(0.001, dtype = tf.float32)
keep_prob = tf.placeholder(tf.float32)
#define nn
W1 = tf.Variable(tf.truncated_normal([784,500], stddev = 0.1))
b1 = tf.Variable(tf.zeros([500]) + 0.1)
L1 = tf.nn.tanh(tf.matmul(x,W1) + b1)
L1_drop = tf.nn.dropout(L1, keep_prob)
W2 = tf.Variable(tf.truncated_normal([500,300], stddev = 0.1))
b2 = tf.Variable(tf.zeros([300]) + 0.1)
L2 = tf.nn.tanh(tf.matmul(L1_drop,W2) + b2)
L2_drop = tf.nn.dropout(L2, keep_prob)
W3 = tf.Variable(tf.truncated_normal([300,100], stddev = 0.1))
b3 = tf.Variable(tf.zeros([100]) + 0.1)
L3 = tf.nn.tanh(tf.matmul(L2_drop,W3) + b3)
L3_drop = tf.nn.dropout(L3, keep_prob)
W4 = tf.Variable(tf.truncated_normal([100,10], stddev = 0.1))
b4 = tf.Variable(tf.zeros([10]) + 0.1)
def main():
image_lists = create_image_lists(TEST_PERCENTAGE, VALIDATION_PERCENTAGE)
n_classes = len(image_lists.keys())
# 读取已经训练好的Inception-v3模型。谷歌训练好的模型包吃住了GraphDef ProtocolBuffer中，里面报错了每一个节点取值的计算方法及其变量的取值
# TensorFlow模型持久化的问题在第5章中有详细的计算
with gfile.FastGFile(os.path.join(MODEL_DIR, MODEL_FILE), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# 加载读取的Inception-v3模型，并返回数据输入所对应的张量以及计算瓶颈层结果对应的张量
bottleneck_tensor, jpeg_data_tensor = tf.import_graph_def(
graph_def, return_elements=[BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME])
# 定义新的神经网络输入，这个输入就是新的图片经过Inception-v3模型前向传播到达瓶颈层
# 时的取值。可以将这个过程类似的理解为一种特征提取
bottleneck_input = tf.placeholder(tf.float32, [None, BOTTLENECK_TENSOR_SIZE], name='BottleneckInputPlaceholder')
# 定义新的标准答案输入
ground_truth_input = tf.placeholder(tf.float32, [None, n_classes], name='GroundTruthInput')
# 定义一层全链接层来解决新的图片分类问题。因为训练好的Inception-v3模型已经将原始的图片抽象为了
# 更加容易分类的特征向量了，所有不需要再训练那么复杂的神经网络来完成新的分类任务
with tf.name_scope('final_training_ops'):
weights = tf.Variable(tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, n_classes], stddev=0.001))
biases = tf.Variable(tf.zeros([n_classes]))
logits = tf.matmul(bottleneck_input, weights) + biases
final_tensor = tf.nn.softmax(logits)
# 定义交叉熵损失函数。
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=ground_truth_input)
cross_entropy_mean = tf.reduce_mean(cross_entropy)
train_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(cross_entropy_mean)
# 计算正确率。
with tf.name_scope('evaluation'):
correct_prediction = tf.equal(tf.argmax(final_tensor, 1), tf.argmax(ground_truth_input, 1))
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
# 训练过程。
for i in range(STEPS):
# 每次获取一个batch的训练数据
train_bottlenecks, train_ground_truth = get_random_cached_bottlenecks(
sess, n_classes, image_lists, BATCH, 'training', jpeg_data_tensor, bottleneck_tensor)
sess.run(train_step,
feed_dict={bottleneck_input: train_bottlenecks, ground_truth_input: train_ground_truth})
# 在验证数据上测试正确率
if i % 100 == 0 or i + 1 == STEPS:
validation_bottlenecks, validation_ground_truth = get_random_cached_bottlenecks(
sess, n_classes, image_lists, BATCH, 'validation', jpeg_data_tensor, bottleneck_tensor)
validation_accuracy = sess.run(evaluation_step, feed_dict={
bottleneck_input: validation_bottlenecks, ground_truth_input: validation_ground_truth})
print('Step %d: Validation accuracy on random sampled %d examples = %.1f%%' %
(i, BATCH, validation_accuracy * 100))
# 在最后的测试数据上测试正确率。
test_bottlenecks, test_ground_truth = get_test_bottlenecks(
sess, image_lists, n_classes, jpeg_data_tensor, bottleneck_tensor)
test_accuracy = sess.run(evaluation_step, feed_dict={
bottleneck_input: test_bottlenecks, ground_truth_input: test_ground_truth})
print('Final test accuracy = %.1f%%' % (test_accuracy * 100))
def initial_cell_state(self, batch: int) -> Tuple[tf.Tensor]:
if self.use_rnn:
return tuple(
tf.zeros((batch, self.h_dim), dtype=tf.float32)
for _ in range(self.cell_nums))
return (None, )
graph = tf.Graph()
with graph.as_default():
# Input data.
train_dataset = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
# Variables.
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
softmax_weights = tf.Variable(
tf.truncated_normal([vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
softmax_biases = tf.Variable(tf.zeros([vocabulary_size]), dtype=tf.float32)
# Model.
# Look up embeddings for inputs.
embed = tf.nn.embedding_lookup(embeddings, train_dataset)
# Compute the softmax loss, using a sample of the negative labels each time.
loss = tf.reduce_mean(tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed,train_labels, num_sampled, vocabulary_size))
# Optimizer.
# Note: The optimizer will optimize the softmax_weights AND the embeddings.
# This is because the embeddings are defined as a variable quantity and the
# optimizer's `minimize` method will by default modify all variable quantities
# that contribute to the tensor it is passed.
# See docs on `tf.train.Optimizer.minimize()` for more details.
optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)
def word2vec(dataset):
""" Build the graph for word2vec model and train it """
# Step 1: create iterator and get input, output from the dataset
iterator = dataset.make_initializable_iterator()
center_words, target_words = iterator.get_next()
#############################
########## TO DO ############
#############################
# Step 2: define weights.
# In word2vec, it's the weights that we care about
embed_matrix = tf.get_variable('embed_matrix',
shape = [VOCAB_SIZE, EMBED_SIZE],
initializer = tf.random_uniform_initializer())
#############################
########## TO DO ############
#############################
# Step 3: define the inference (embedding lookup)
embed = tf.nn.embedding_lookup(embed_matrix, center_words, name='embed')
#############################
########## TO DO ############
#############################
# Step 4: define loss function
# construct variables for NCE loss
nce_weight = tf.get_variable('nce_weight',
shape = [VOCAB_SIZE, EMBED_SIZE],
initializer = tf.truncated_normal_initializer(stddev=1.0 / (EMBED_SIZE ** 0.5)))
nce_bias = tf.get_variable('nce_bias', initializer = tf.zeros([VOCAB_SIZE]))
#############################
########## TO DO ############
#############################
# define loss function to be NCE loss function
loss = tf.reduce_mean(tf.nn.nce_loss(weights = nce_weight,
biases = nce_bias,
labels = target_words,
inputs = embed,
num_sampled = NUM_SAMPLED,
num_classes = VOCAB_SIZE), name = 'loss')
#############################
########## TO DO ############
#############################
# Step 5: define optimizer that follows gradient descent update rule
# to minimize loss
optimizer = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(loss)
#############################
########## TO DO ############
#############################
utils.safe_mkdir('checkpoints')
with tf.Session() as sess:
# Step 6: initialize iterator and variables
sess.run(iterator.initializer)
sess.run(tf.global_variables_initializer())
#############################
########## TO DO ############
#############################
total_loss = 0.0 # we use this to calculate late average loss in the last SKIP_STEP steps
writer = tf.summary.FileWriter('graphs/word2vec_simple', sess.graph)
for index in range(NUM_TRAIN_STEPS):
try:
# Step 7: execute optimizer and fetch loss
loss_batch, _ = sess.run([loss, optimizer])
#############################
########## TO DO ############
#############################
total_loss += loss_batch
if (index + 1) % SKIP_STEP == 0:
print('Average loss at step {}: {:5.1f}'.format(index, total_loss / SKIP_STEP))
total_loss = 0.0
except tf.errors.OutOfRangeError:
sess.run(iterator.initializer)
writer.close()
def init_bias(self, dim_out, name=None):
return tf.Variable(tf.zeros([dim_out]), name=name)
net = tf_util.max_pool2d(net, [num_point,1],
padding='VALID', scope='maxpool')
# MLP on global point cloud vector
net = tf.reshape(net, [batch_size, -1])
net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training,
scope='fc1', bn_decay=bn_decay)
net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training,
scope='fc2', bn_decay=bn_decay)
net = tf_util.dropout(net, keep_prob=0.7, is_training=is_training,
scope='dp1')
net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')
return net, end_points
def get_loss(pred, label, end_points):
""" pred: B*NUM_CLASSES,
label: B, """
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
classify_loss = tf.reduce_mean(loss)
tf.summary.scalar('classify loss', classify_loss)
return classify_loss
if __name__=='__main__':
with tf.Graph().as_default():
inputs = tf.zeros((32,1024,3))
outputs = get_model(inputs, tf.constant(True))
print(outputs)
def construct_sampling_ops(model):
"""Builds a graph fragment for sampling over a RNNModel.
Args:
model: a RNNModel.
Returns:
A Tensor with shape (max_seq_len, batch_size) containing one sampled
translation for each input sentence in model.inputs.x.
"""
decoder = model.decoder
batch_size = tf.shape(decoder.init_state)[0]
high_depth = 0 if decoder.high_gru_stack == None \
else len(decoder.high_gru_stack.grus)
i = tf.constant(0)
init_y = -tf.ones(dtype=tf.int32, shape=[batch_size])
init_emb = tf.zeros(dtype=tf.float32,
shape=[batch_size, decoder.embedding_size])
y_array = tf.TensorArray(
dtype=tf.int32,
size=decoder.translation_maxlen,
clear_after_read=
True, #TODO: does this help? or will it only introduce bugs in the future?
name='y_sampled_array')
init_loop_vars = [
i, decoder.init_state, [decoder.init_state] * high_depth, init_y,
init_emb, y_array
]
def cond(i, base_state, high_states, prev_y, prev_emb, y_array):
return tf.logical_and(tf.less(i, decoder.translation_maxlen),
tf.reduce_any(tf.not_equal(prev_y, 0)))
def body(i, prev_base_state, prev_high_states, prev_y, prev_emb, y_array):
state1 = decoder.grustep1.forward(prev_base_state, prev_emb)
att_ctx = decoder.attstep.forward(state1)
base_state = decoder.grustep2.forward(state1, att_ctx)
if decoder.high_gru_stack == None:
output = base_state
high_states = []
else:
if decoder.high_gru_stack.context_state_size == 0:
output, high_states = decoder.high_gru_stack.forward_single(
prev_high_states, base_state)
else:
output, high_states = decoder.high_gru_stack.forward_single(
prev_high_states, base_state, context=att_ctx)
logits = decoder.predictor.get_logits(prev_emb,
output,
att_ctx,
multi_step=False)
new_y = tf.multinomial(logits, num_samples=1)
new_y = tf.cast(new_y, dtype=tf.int32)
new_y = tf.squeeze(new_y, axis=1)
new_y = tf.where(tf.equal(prev_y, tf.constant(0, dtype=tf.int32)),
tf.zeros_like(new_y), new_y)
y_array = y_array.write(index=i, value=new_y)
new_emb = decoder.y_emb_layer.forward(new_y, factor=0)
return i + 1, base_state, high_states, new_y, new_emb, y_array
final_loop_vars = tf.while_loop(cond=cond,
body=body,
loop_vars=init_loop_vars,
back_prop=False)
i, _, _, _, _, y_array = final_loop_vars
sampled_ys = y_array.gather(tf.range(0, i))
return sampled_ys
def build_model(self):
context = tf.placeholder("float32", [self.batch_size, self.ctx_shape[0], self.ctx_shape[1]])
sentence = tf.placeholder("int32", [self.batch_size, self.n_lstm_steps])
mask = tf.placeholder("float32", [self.batch_size, self.n_lstm_steps])
h, c = self.get_initial_lstm(tf.reduce_mean(context, 1))
# TensorFlow가 dot(3D tensor, matrix) 계산을 못함;;; ㅅㅂ 삽질 ㄱㄱ
context_flat = tf.reshape(context, [-1, self.dim_ctx])
context_encode = tf.matmul(context_flat, self.image_att_W) # (batch_size, 196, 512)
context_encode = tf.reshape(context_encode, [-1, ctx_shape[0], ctx_shape[1]])
loss = 0.0
for ind in range(self.n_lstm_steps):
if ind == 0:
word_emb = tf.zeros([self.batch_size, self.dim_embed])
else:
tf.get_variable_scope().reuse_variables()
with tf.device("/cpu:0"):
word_emb = tf.nn.embedding_lookup(self.Wemb, sentence[:,ind-1])
x_t = tf.matmul(word_emb, self.lstm_W) + self.lstm_b # (batch_size, hidden*4)
labels = tf.expand_dims(sentence[:,ind], 1)
indices = tf.expand_dims(tf.range(0, self.batch_size, 1), 1)
concated = tf.concat(1, [indices, labels])
onehot_labels = tf.sparse_to_dense( concated, tf.pack([self.batch_size, self.n_words]), 1.0, 0.0)
context_encode = context_encode + \
tf.expand_dims(tf.matmul(h, self.hidden_att_W), 1) + \
self.pre_att_b
context_encode = tf.nn.tanh(context_encode)
# 여기도 context_encode: 3D -> flat required
context_encode_flat = tf.reshape(context_encode, [-1, self.dim_ctx]) # (batch_size*196, 512)
alpha = tf.matmul(context_encode_flat, self.att_W) + self.att_b # (batch_size*196, 1)
alpha = tf.reshape(alpha, [-1, self.ctx_shape[0]])
alpha = tf.nn.softmax( alpha )
weighted_context = tf.reduce_sum(context * tf.expand_dims(alpha, 2), 1)
lstm_preactive = tf.matmul(h, self.lstm_U) + x_t + tf.matmul(weighted_context, self.image_encode_W)
i, f, o, new_c = tf.split(1, 4, lstm_preactive)
i = tf.nn.sigmoid(i)
f = tf.nn.sigmoid(f)
o = tf.nn.sigmoid(o)
new_c = tf.nn.tanh(new_c)
c = f * c + i * new_c
h = o * tf.nn.tanh(new_c)
logits = tf.matmul(h, self.decode_lstm_W) + self.decode_lstm_b
logits = tf.nn.relu(logits)
logits = tf.nn.dropout(logits, 0.5)
logit_words = tf.matmul(logits, self.decode_word_W) + self.decode_word_b
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logit_words, onehot_labels)
cross_entropy = cross_entropy * mask[:,ind]
current_loss = tf.reduce_sum(cross_entropy)
loss = loss + current_loss
loss = loss / tf.reduce_sum(mask)
return loss, context, sentence, mask
_, csv_row = reader.read(filename_queue)
record_defaults = [[1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1]]
col1, col2, col3, col4, col5, col6, col7, col8, col9, col10, col11 = tf.decode_csv(
csv_row, record_defaults=record_defaults)
features = tf.stack([col1, col2, col3, col4, col5, col6, col7, col8, col9])
label = tf.stack([col10, col11])
return features, label
filename_queue = tf.train.string_input_producer(["COMBINED_DATA.csv"])
features, labels = create_file_reader_ops(filename_queue)
x = tf.placeholder(tf.float32, shape=[None, 9])
y_ = tf.placeholder(tf.float32, shape=[None, 2])
W = tf.Variable(tf.zeros([9, 2]))
b = tf.Variable(tf.zeros([2]))
y = tf.matmul(x, W) + b
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
start = time.time()
for i in range(500):
def construct_beam_search_ops(models, beam_size):
"""Builds a graph fragment for beam search over one or more RNNModels.
Strategy:
compute the log_probs - same as with sampling
for sentences that are ended set log_prob(<eos>)=0, log_prob(not eos)=-inf
add previous cost to log_probs
run top k -> (idxs, values)
use values as new costs
divide idxs by num_classes to get state_idxs
use gather to get new states
take the remainder of idxs after num_classes to get new_predicted words
"""
# Get some parameter settings. For ensembling, some parameters are required
# to be consistent across all models but others are not. In the former
# case, we assume that consistency has already been checked. For the
# parameters that are allowed to vary across models, the first model's
# settings take precedence.
decoder = models[0].decoder
batch_size = tf.shape(decoder.init_state)[0]
embedding_size = decoder.embedding_size
translation_maxlen = decoder.translation_maxlen
target_vocab_size = decoder.target_vocab_size
high_depth = 0 if decoder.high_gru_stack == None \
else len(decoder.high_gru_stack.grus)
# Initialize loop variables
i = tf.constant(0)
init_ys = -tf.ones(dtype=tf.int32, shape=[batch_size])
init_embs = [
tf.zeros(dtype=tf.float32, shape=[batch_size, embedding_size])
] * len(models)
f_min = numpy.finfo(numpy.float32).min
init_cost = [0.] + [f_min] * (
beam_size - 1) # to force first top k are from first hypo only
init_cost = tf.constant(init_cost, dtype=tf.float32)
init_cost = tf.tile(init_cost,
multiples=[batch_size / beam_size]) # duplications
ys_array = tf.TensorArray(dtype=tf.int32,
size=translation_maxlen,
clear_after_read=True,
name='y_sampled_array')
p_array = tf.TensorArray(dtype=tf.int32,
size=translation_maxlen,
clear_after_read=True,
name='parent_idx_array')
init_base_states = [m.decoder.init_state for m in models]
init_high_states = [[m.decoder.init_state] * high_depth for m in models]
init_loop_vars = [
i, init_base_states, init_high_states, init_ys, init_embs, init_cost,
ys_array, p_array
]
# Prepare cost matrix for completed sentences -> Prob(EOS) = 1 and Prob(x) = 0
eos_log_probs = tf.constant([[0.] + ([f_min] * (target_vocab_size - 1))],
dtype=tf.float32)
eos_log_probs = tf.tile(eos_log_probs, multiples=[batch_size, 1])
def cond(i, prev_base_states, prev_high_states, prev_ys, prev_embs, cost,
ys_array, p_array):
return tf.logical_and(tf.less(i, translation_maxlen),
tf.reduce_any(tf.not_equal(prev_ys, 0)))
def body(i, prev_base_states, prev_high_states, prev_ys, prev_embs, cost,
ys_array, p_array):
# get predictions from all models and sum the log probs
sum_log_probs = None
base_states = [None] * len(models)
high_states = [None] * len(models)
for j in range(len(models)):
d = models[j].decoder
states1 = d.grustep1.forward(prev_base_states[j], prev_embs[j])
att_ctx = d.attstep.forward(states1)
base_states[j] = d.grustep2.forward(states1, att_ctx)
if d.high_gru_stack == None:
stack_output = base_states[j]
high_states[j] = []
else:
if d.high_gru_stack.context_state_size == 0:
stack_output, high_states[
j] = d.high_gru_stack.forward_single(
prev_high_states[j], base_states[j])
else:
stack_output, high_states[
j] = d.high_gru_stack.forward_single(
prev_high_states[j],
base_states[j],
context=att_ctx)
logits = d.predictor.get_logits(prev_embs[j],
stack_output,
att_ctx,
multi_step=False)
log_probs = tf.nn.log_softmax(logits) # shape (batch, vocab_size)
if sum_log_probs == None:
sum_log_probs = log_probs
else:
sum_log_probs += log_probs
# set cost of EOS to zero for completed sentences so that they are in top k
# Need to make sure only EOS is selected because a completed sentence might
# kill ongoing sentences
sum_log_probs = tf.where(tf.equal(prev_ys, 0), eos_log_probs,
sum_log_probs)
all_costs = sum_log_probs + tf.expand_dims(
cost, axis=1
) # TODO: you might be getting NaNs here since -inf is in log_probs
all_costs = tf.reshape(all_costs,
shape=[-1, target_vocab_size * beam_size])
values, indices = tf.nn.top_k(
all_costs, k=beam_size
) #the sorted option is by default True, is this needed?
new_cost = tf.reshape(values, shape=[batch_size])
offsets = tf.range(start=0,
delta=beam_size,
limit=batch_size,
dtype=tf.int32)
offsets = tf.expand_dims(offsets, axis=1)
survivor_idxs = (indices / target_vocab_size) + offsets
new_ys = indices % target_vocab_size
survivor_idxs = tf.reshape(survivor_idxs, shape=[batch_size])
new_ys = tf.reshape(new_ys, shape=[batch_size])
new_embs = [
m.decoder.y_emb_layer.forward(new_ys, factor=0) for m in models
]
new_base_states = [
tf.gather(s, indices=survivor_idxs) for s in base_states
]
new_high_states = [[
tf.gather(s, indices=survivor_idxs) for s in states
] for states in high_states]
new_cost = tf.where(tf.equal(new_ys, 0), tf.abs(new_cost), new_cost)
ys_array = ys_array.write(i, value=new_ys)
p_array = p_array.write(i, value=survivor_idxs)
return i + 1, new_base_states, new_high_states, new_ys, new_embs, new_cost, ys_array, p_array
final_loop_vars = tf.while_loop(cond=cond,
body=body,
loop_vars=init_loop_vars,
back_prop=False)
i, _, _, _, _, cost, ys_array, p_array = final_loop_vars
indices = tf.range(0, i)
sampled_ys = ys_array.gather(indices)
parents = p_array.gather(indices)
cost = tf.abs(cost) #to get negative-log-likelihood
return sampled_ys, parents, cost
def log_regression():
learning_rate = 0.01
batch_size = 1024
iter_num = 30
display_step = 1
beta = 0.1
threshold = 0.5
seed = 5
np.random.seed(seed)
tf.set_random_seed(seed)
print("Loading training data...")
train_data = pd.read_csv(currentDirectory + "train\\" +
"CombinedTrainData.csv",
header=None)
print("Shuffling training data...")
train_data = train_data.iloc[np.random.permutation(len(train_data))]
train_X = train_data.iloc[:, 0:number_of_columns -
1] #selecting the feature columns
train_X = np.array(train_X)
train_Y = np.array(train_data.iloc[:, number_of_columns -
1:]) # selecting the label column
train_Y = np.array(train_Y)
# print ("Loading validation data...")
# dev_data = pd.read_csv(currentDirectory + "dev\\" + "CombinedDevData.csv")
# dev_X = dev_data.iloc[:,0:number_of_columns-1] #selecting the feature columns
# #dev_X = min_max_normalized(np.array(dev_X))
# dev_Y = np.array(dev_data.iloc[:,number_of_columns-1:]) # selecting the label column
print("Loading test data...")
test_data = pd.read_csv(currentDirectory + "test\\" +
"CombinedTestData.csv")
test_X = test_data.iloc[:, 0:number_of_columns -
1] #selecting the feature columns
test_X = np.array(test_X)
#dev_X = min_max_normalized(np.array(dev_X))
test_Y = np.array(test_data.iloc[:, number_of_columns -
1:]) # selecting the label column
test_Y = np.array(test_Y)
#Defining the model framework
w = tf.Variable(tf.zeros([44, 1]), name="weights")
b = tf.Variable(tf.zeros([1]), name="bias")
X = tf.placeholder(tf.float32, [None, 44], name='data')
Y = tf.placeholder(tf.float32, [None, 1], name='target')
pred = tf.sigmoid(tf.matmul(X, w) + b)
# Minimize error using cross entropy (1st cost function)
#cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=pred, labels=Y))
# (2nd cost function used)
#cost = tf.multiply(-1.0, tf.reduce_mean(tf.add(tf.multiply(beta,tf.multiply(Y,tf.log(pred))),tf.multiply(tf.subtract(1.0, Y) , tf.log(tf.subtract(1.0, pred))))))
cost = tf.reduce_mean(
tf.nn.weighted_cross_entropy_with_logits(logits=pred,
targets=Y,
pos_weight=beta))
#To calculate precision, which is the main metric
rounded_pred = tf.cast(pred >= threshold, dtype=tf.float32)
total_ones = tf.reduce_sum(rounded_pred)
true_positive = tf.reduce_sum(tf.multiply(rounded_pred, Y))
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=learning_rate).minimize(cost)
#correct = tf.cast(tf.equal(tf.round(pred), Y), dtype=tf.float32)
#accuracy = tf.metrics.precision(labels = Y, predictions = tf.round(pred))
writer = tf.summary.FileWriter('./graphs', tf.get_default_graph())
train_precison = []
test_precision = []
init = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
with tf.Session() as sess:
sess.run(init)
sess.run(init_l)
for epoch in range(iter_num):
avg_cost = 0.0
tp = 0
tp_fp = 0.000000000001 # to prevent division by 0
test_tp = 0
test_tp_fp = 0.000000000001 # to prevent division by 0
total_batch = math.ceil(train_X.shape[0] / batch_size)
for i in range(total_batch - 1):
batch_xs = train_X[i * batch_size:batch_size * i +
batch_size, :]
batch_ys = train_Y[i * batch_size:batch_size * i +
batch_size, :]
test_xs = test_X[i * batch_size:batch_size * i + batch_size, :]
test_ys = test_Y[i * batch_size:batch_size * i + batch_size, :]
# # convert into a matrix, and the shape of the placeholder to correspond
# temp_train_acc = sess.run(accuracy, feed_dict={X: batch_xs, Y: batch_ys})
# temp_test_acc = sess.run(accuracy, feed_dict={X: dev_X, Y: dev_Y})
# # recode the result
# loss_trace.append(temp_loss)
# train_acc.append(temp_train_acc)
# validation_acc.append(temp_test_acc)
# output
# Run optimization op (backprop) and cost op (to get loss value)
#_, c, p, temp_train_acc = sess.run([optimizer, cost, pred, accuracy], feed_dict={X: batch_xs, Y: batch_ys})
_, c, p, ones, correct_ones = sess.run(
[optimizer, cost, pred, total_ones, true_positive],
feed_dict={
X: batch_xs,
Y: batch_ys
})
test_ones, test_correct_ones = sess.run(
[total_ones, true_positive],
feed_dict={
X: test_xs,
Y: test_ys
})
avg_cost += c
tp += correct_ones
tp_fp += ones
test_tp += test_correct_ones
test_tp_fp += test_ones
#including the last batch
batch_xs = train_X[batch_size * (total_batch - 1):, :]
batch_ys = train_Y[batch_size * (total_batch - 1):, :]
#_, c, p = sess.run([optimizer, cost, pred], feed_dict={X: batch_xs, Y: batch_ys})
#_, c, p, temp_train_acc = sess.run([optimizer, cost, pred, accuracy], feed_dict={X: batch_xs, Y: batch_ys})
_, c, p, ones, correct_ones = sess.run(
[optimizer, cost, pred, total_ones, true_positive],
feed_dict={
X: batch_xs,
Y: batch_ys
})
test_ones, test_correct_ones = sess.run(
[total_ones, true_positive],
feed_dict={
X: test_xs,
Y: test_ys
})
avg_cost += c
tp += correct_ones
tp_fp += ones
test_tp += test_correct_ones
test_tp_fp += test_ones
#temp_train_acc = sess.run(accuracy, feed_dict={X: batch_xs, Y: batch_ys})
#train_acc.append(temp_train_acc[0])
#print ("Tensorflow precision: ", temp_train_acc[0])
print("Train Precision: ", float(tp) / float(tp_fp))
print("Test Precision: ", float(test_tp) / float(test_tp_fp))
# Display logs per epoch step
if (epoch + 1) % display_step == 0:
#print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
#print('epoch: {:4d} cost = : {:.9f} train_precision: {:.9f} '.format(epoch + 1, avg_cost, temp_train_acc[0]))
print(
'epoch: {:4d} cost = : {:.9f} train_precision: {:.9f} test_precision: {:.9f}'
.format(epoch + 1, avg_cost,
float(tp) / float(tp_fp),
float(test_tp) / float(test_tp_fp)))
print("Optimization Finished!")
writer.close()
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
import tensorflow as tf
import numpy as np
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.global_variables_initializer())
y = tf.matmul(x, W) + b
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
for i in range(1000):
batch = mnist.train.next_batch(100)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
print("training complete")
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
def _create_variables(self):
self.global_step = tf.Variable(
initial_value=0,
trainable=False
)
boundaries = np.arange(0, self.epoch, self.anneal_every) * self.steps_per_epoch
learning_rates = self.lr / (2 ** np.arange(len(boundaries) + 1))
self.learning_rate = tf.train.piecewise_constant(
self.global_step,
list(boundaries.astype(np.int32)),
[self.lr] + list(learning_rates.astype(np.float32))
)
J, D = self.sentence_length, self.embedding_size
j = np.expand_dims(np.linspace(1, J, J), 0)
k = np.expand_dims(np.linspace(1, D, D), 1)
l = (1 - j / J) - k / D * (1 - 2 * j / J)
self.l_pe = tf.constant(l, tf.float32, shape=[J, D])
with tf.variable_scope('input'):
# sentences - stories - facts
self.x = tf.placeholder(tf.int32,
[None, self.memory_size, self.sentence_length],
name='facts')
self.q = tf.placeholder(tf.int32, [None, self.sentence_length], name='query')
self.a = tf.placeholder(tf.int32, [None], name='answer')
self.embeddings = {}
with tf.variable_scope('embeddings'):
# +1 is for fixed zero input
zero_embedding = tf.zeros([1, self.embedding_size])
B = tf.concat(0, [
zero_embedding,
tf.get_variable('B', [self.vocab_size, self.embedding_size], tf.float32),
])
self.embeddings['B'] = B
C_prev = B
TC_prev = tf.get_variable('TB', [self.memory_size, self.embedding_size], tf.float32)
for k in range(1, self.hops + 1):
k = str(k)
with tf.variable_scope('hop' + k):
C = tf.concat(0, [
zero_embedding,
tf.get_variable('C', [self.vocab_size, self.embedding_size], tf.float32)
])
TC = tf.get_variable('TC', [self.memory_size, self.embedding_size], tf.float32)
self.embeddings['A' + k] = C_prev
self.embeddings['TA' + k] = TC_prev
self.embeddings['C' + k] = C
self.embeddings['TC' + k] = TC
C_prev = C
TC_prev = TC
tf.histogram_summary('A' + k, self.embeddings['A' + k])
tf.histogram_summary('C' + k, C)
self.embeddings['W'] = tf.transpose(self.embeddings['C' + str(self.hops)])
labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
# word2vec 모델의 결과 값인 임베딩 벡터를 저장할 변수입니다.
# 총 단어 갯수와 임베딩 갯수를 크기로 하는 두 개의 차원을 갖습니다.
embeddings = tf.Variable(tf.random_uniform([voc_size, embedding_size], -1.0, 1.0), name='embeddings')
# 임베딩 벡터의 차원에서 학습할 입력값에 대한 행들을 뽑아옵니다.
# 예) embeddings inputs selected
# [[1, 2, 3] -> [2, 3] -> [[2, 3, 4]
# [2, 3, 4] [3, 4, 5]]
# [3, 4, 5]
# [4, 5, 6]]
selected_embed = tf.nn.embedding_lookup(embeddings, inputs, name='selected_embed')
# nce_loss 함수에서 사용할 변수들을 정의합니다.
nce_weights = tf.Variable(tf.random_uniform([voc_size, embedding_size], -1.0, 1.0), name='nce_weights')
nce_biases = tf.Variable(tf.zeros([voc_size]), name='nce_biases')
# nce_loss 함수를 직접 구현하려면 매우 복잡하지만,
# 함수를 텐서플로우가 제공하므로 그냥 tf.nn.nce_loss 함수를 사용하기만 하면 됩니다.
loss = tf.reduce_mean(
tf.nn.nce_loss(nce_weights, nce_biases, labels, selected_embed, num_sampled, voc_size))
train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss)
#########
# 신경망 모델 학습
######
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
h=encoder_final_state_h)
#define decoder with minimal use of predefined functions (going to be longer)
with tf.variable_scope('decoding') as decoding_scope:
decoder_cell = LSTMCell(decoder_hidden_units)
encoder_max_time, batch_size = tf.unstack(tf.shape(encoder_inputs))
decoder_lengths = encoder_inputs_length + 3
#output projection
#define our weights and biases
W = tf.Variable(tf.random_uniform(shape=[decoder_hidden_units, vocab_size],
minval=-1,
maxval=1),
dtype=tf.float32)
b = tf.Variable(tf.zeros([vocab_size]), dtype=tf.float32)
#For padding and EOS
eos_time_slice = tf.ones([batch_size], dtype=tf.int32, name='EOS')
pad_time_slice = tf.zeros([batch_size], dtype=tf.int32, name='PAD')
eos_step_embedded = tf.nn.embedding_lookup(embeddings, eos_time_slice)
pad_step_embedded = tf.nn.embedding_lookup(embeddings, pad_time_slice)
def loop_fn_initial():
initial_elements_finished = (0 >= decoder_lengths)
initial_input = eos_step_embedded
initial_cell_state = encoder_final_state
initial_cell_output = None
initial_loop_state = None
def forward(self, x, x_mask=None, context_layer=None, init_state=None):
assert not (self.reverse_alternation and x_mask == None)
# assert (context_layer == None or
# tf.shape(context_layer)[-1] == self.context_state_size)
def create_step_fun(gru):
def step_fn(prev_state, x):
gates_x2d, proposal_x2d = x[0], x[1]
new_state = gru.forward(prev_state,
gates_x=gates_x2d,
proposal_x=proposal_x2d)
if len(x) > 2:
mask = x[2]
new_state *= mask # batch x 1
# first couple of states of reversed encoder should be zero
# this is why we need to multiply by mask
# this way, when the reversed encoder reaches actual words
# the state will be zeros and not some accumulated garbage
return new_state
return step_fn
if init_state is None:
init_state = tf.zeros(shape=[self.batch_size, self.state_size],
dtype=tf.float32)
if x_mask != None:
x_mask_r = tf.reverse(x_mask, axis=[0])
x_mask_bwd = tf.expand_dims(x_mask_r, axis=[2]) #seqLen x batch x 1
for i, gru in enumerate(self.grus):
layer = RecurrentLayer(initial_state=init_state,
step_fn=create_step_fun(gru))
if context_layer == None:
x2 = x
else:
x2 = tf.concat([x, context_layer], axis=-1)
if not self.alternating:
left_to_right = True
else:
if self.reverse_alternation:
left_to_right = (i % 2 == 1)
else:
left_to_right = (i % 2 == 0)
if left_to_right:
# Recurrent state flows from left to right in this layer.
gates_x, proposal_x = gru.precompute_from_x(x2)
h = layer.forward((gates_x, proposal_x))
else:
# Recurrent state flows from right to left in this layer.
x2_reversed = tf.reverse(x2, axis=[0])
gates_x, proposal_x = gru.precompute_from_x(x2_reversed)
h_reversed = layer.forward((gates_x, proposal_x, x_mask_bwd))
h = tf.reverse(h_reversed, axis=[0])
# Compute the word states, which will become the input for the
# next layer (or the output of the stack if we're at the top).
if not self.residual_connections or i < self.first_residual_output:
x = h
else:
x += h # Residual connection
return x
# naive training, train a list of parameter (W & b) # with these settings, the result will converge by 2500 steps import tensorflow as tf import numpy as np # construct data set target = [0.123, 0.234, 0.345, 0.456, 0.567, 0.678, 0.789, 0.898, 0.987, 10.654] _x = np.float32(np.random.rand(10, 1000)) _y = np.dot(target, _x) + 0.233 b = tf.Variable(tf.zeros([1])) W = tf.Variable(tf.random_uniform(shape = [1, 10], minval = -100.0, maxval = 100.0)) y = tf.matmul(W, _x) + b # set loss loss = tf.reduce_mean(tf.square(y - _y)) # set optimizer, namely gradient descent mode # when the learning rate is too high, the descent will "boom" and get "nan" optimizer = tf.train.GradientDescentOptimizer(0.15) # set the target of training train = optimizer.minimize(loss) # init init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) # training
train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
# Ops and variables pinned to the CPU because of missing GPU implementation
with tf.device('/cpu:0'):
# Look up embeddings for inputs.
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
# Construct the variables for the NCE loss
nce_weights = tf.Variable(
tf.truncated_normal([vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
# Compute the average NCE loss for the batch.
# tf.nce_loss automatically draws a new sample of the negative labels each
# time we evaluate the loss.
loss = tf.reduce_mean(
tf.nn.nce_loss(weights=nce_weights,
biases=nce_biases,
labels=train_labels,
inputs=embed,
num_sampled=num_sampled,
num_classes=vocabulary_size))
# Construct the SGD optimizer using a learning rate of 1.0.
optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
image = tf.placeholder(tf.float32, [None, 224, 224, 3])
image = tf.div(tf.subtract(image, tf.reduce_mean(image)), reduce_std(image))
labels = tf.placeholder(tf.float32, [None, 54])
net = VGG_CNN_F({'data': image})
fc7 = net.layers['fc7']
print fc7.get_shape()
w1 = tf.Variable(tf.random_normal(shape=[4096, 100],
mean=0,
stddev=2 / np.sqrt(4096)),
name='feature_layer')
b1 = tf.Variable(tf.zeros([100]), name='feature_layer_bias')
output = tf.matmul(fc7, w1) + b1
w = tf.Variable(tf.random_normal(shape=[100, 54],
mean=0,
stddev=2 / np.sqrt(100)),
name='last_W')
b = tf.Variable(tf.zeros([54]), name='last_b')
output1 = tf.matmul(output, w) + b
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=output))
tf.summary.scalar('Cost', cost)
opt = tf.train.AdamOptimizer(learning_rate=0.00001)
train_op = opt.minimize(cost)
merged = tf.summary.merge_all()
trainData = mnist.data[:60000].astype(float) / 255.
trainLabels = mnist.target[:60000]
testData = mnist.data[60000:].astype(float) / 255.
testLabels = mnist.target[60000:]
# Create the model
tf.reset_default_graph() # reset if we are rerunning code to avoid variable re-use
# Input layer
x = tf.placeholder(tf.float32, [None, 784])
# Hidden layer
#W1 = tf.get_variable('W1', [784, 100], initializer=tf.random_normal_initializer())
#W1 = tf.Variable(tf.random_normal([784, 100]))
W1 = tf.Variable(tf.truncated_normal([784, 100], stddev=1.0 / np.sqrt(784)))
b1 = tf.Variable(tf.zeros([100,]))
z1 = tf.matmul(x, W1) + b1
y1 = tf.nn.tanh(z1)
# Output layer
#W2 = tf.Variable(tf.random_normal([100, 10]))
W2 = tf.Variable(tf.truncated_normal([100, 10], stddev=1.0 / np.sqrt(100)))
b2 = tf.Variable(tf.zeros([10,]))
y2 = tf.matmul(y1, W2) + b2
# Define the output
y = y2
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
fake_box_predictor = tf.train.Checkpoint(
_base_tower_layers_for_heads=detection_model._box_predictor.
_base_tower_layers_for_heads,
# _prediction_heads=detection_model._box_predictor._prediction_heads,
# (i.e., the classification head that we *will not* restore)
_box_prediction_head=detection_model._box_predictor._box_prediction_head,
)
fake_model = tf.train.Checkpoint(
_feature_extractor=detection_model._feature_extractor,
_box_predictor=fake_box_predictor)
ckpt = tf.train.Checkpoint(model=fake_model)
ckpt.restore(checkpoint_path).expect_partial()
# Run model through a dummy image so that variables are created
image, shapes = detection_model.preprocess(tf.zeros([1, 640, 640, 3]))
prediction_dict = detection_model.predict(image, shapes)
_ = detection_model.postprocess(prediction_dict, shapes)
print('Weights restored!')
##########################################################################################
# tf.keras.backend.set_learning_phase(True)
# These parameters can be tuned; since our training set has 5 images
# it doesn't make sense to have a much larger batch size, though we could
# fit more examples in memory if we wanted to.
batch_size = 4
learning_rate = 0.01
num_batches = 100
# Select variables in top layers to fine-tune.
def sample_sparse_grid_like(gt_flow, target_density=75, height=384, width=512):
print("sample_sparse_grid_like")
# Important: matches is already normalised to [0, 1]
num_samples = tf.multiply(
tf.multiply(tf.divide(target_density, 100.0), height), width)
aspect_ratio = tf.divide(width, height)
# Compute as in invalid_like for a random box to know the number of samples in horizontal and vertical
num_samples_w = tf.cast(tf.round(
tf.sqrt(tf.multiply(num_samples, aspect_ratio))),
dtype=tf.int32)
num_samples_h = tf.cast(tf.round(
tf.divide(tf.cast(num_samples_w, dtype=tf.float32), aspect_ratio)),
dtype=tf.int32)
# Check crop dimensions are plausible, otherwise crop them to fit (this alters the density we were sampling at)
num_samples_h = tf.cond(tf.greater(num_samples_h, tf.constant(height)),
lambda: tf.constant(height, dtype=tf.int32),
lambda: num_samples_h)
num_samples_w = tf.cond(tf.greater(num_samples_w, tf.constant(width)),
lambda: tf.constant(width, dtype=tf.int32),
lambda: num_samples_w)
delta_rows = tf.cast((height - 1 - 0) / num_samples_h, tf.float32)
sample_points_h = tf.cast(tf.round(
tf.range(start=0, limit=height, delta=delta_rows, dtype=tf.float32)),
dtype=tf.int32)
delta_cols = tf.cast((width - 1 - 0) / num_samples_w, tf.float32)
sample_points_w = tf.cast(tf.round(
tf.range(start=0, limit=width, delta=delta_cols, dtype=tf.float32)),
dtype=tf.int32)
# Create meshgrid of all combinations (i.e.: coordinates to sample at)
rows, cols = tf.meshgrid(sample_points_h, sample_points_w, indexing='ij')
rows_flatten = tf.reshape(rows, [-1])
cols_flatten = tf.reshape(cols, [-1])
# Compute absolute indices as row * width + cols
indices = tf.add(tf.multiply(rows_flatten, width), cols_flatten)
ones = tf.ones(tf.shape(indices), dtype=tf.float32)
zeros = lambda: tf.zeros((height * width), dtype=tf.float32)
matches = tf.Variable(initial_value=zeros, trainable=False)
matches = tf.scatter_update(matches, indices, ones) # all 1D tensors
# Randomly subtract a part with a random rectangle (superpixels in the future)
corrupt_mask = tf.random_uniform([], maxval=2, dtype=tf.int32)
matches = tf.cond(
tf.greater(corrupt_mask,
tf.constant(0)), lambda: corrupt_sparse_flow_once(
matches, target_density, height, width),
lambda: return_identity_one(matches))
sampling_mask = tf.reshape(matches,
(height, width)) # sampling_mask of size (h, w)
matches = tf.cast(tf.expand_dims(sampling_mask, -1),
dtype=tf.float32) # convert to (h, w, 1)
# Sample ground truth flow with given map
# sampling_mask = sampling_mask[:, :, tf.newaxis]
# sampling_mask_rep = tf.tile(sampling_mask, [1, 1, 2])
# sampling_mask_flatten = tf.reshape(sampling_mask_rep, [-1])
# sampling_mask_flatten_where = tf.where(
# tf.equal(sampling_mask_flatten, tf.cast(1, dtype=sampling_mask_flatten.dtype)))
# sampling_mask_flatten_where = tf.reshape(sampling_mask_flatten_where, [-1])
#
# gt_flow_sampling_mask = tf.boolean_mask(gt_flow, sampling_mask_rep)
# zeros = lambda: tf.zeros(tf.reduce_prod(gt_flow.shape), dtype=tf.float32)
# sparse_flow = tf.Variable(initial_value=zeros, dtype=tf.float32, trainable=False)
# sparse_flow = tf.scatter_update(sparse_flow, sampling_mask_flatten_where, gt_flow_sampling_mask)
# sparse_flow = tf.reshape(sparse_flow, gt_flow.shape)
sparse_flow = mask_to_sparse_flow(sampling_mask, gt_flow)
return matches, sparse_flow