Python tflearn.input_data() Examples
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code examples of tflearn.input_data().
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Example #1
| Source File: models.py From pygta5 with GNU General Public License v3.0 | 8 votes |
|
def resnext(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
net = input_data(shape=[None, width, height, 3], name='input')
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.layers.conv.resnext_block(net, n, 16, 32)
net = tflearn.resnext_block(net, 1, 32, 32, downsample=True)
net = tflearn.resnext_block(net, n-1, 32, 32)
net = tflearn.resnext_block(net, 1, 64, 32, downsample=True)
net = tflearn.resnext_block(net, n-1, 64, 32)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, output, activation='softmax')
opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=opt,
loss='categorical_crossentropy')
model = tflearn.DNN(net,
max_checkpoints=0, tensorboard_verbose=0, tensorboard_dir='log')
return model
Example #2
| Source File: a3c.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def create_critic_network(self):
with tf.variable_scope('critic'):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]])
_input = tf.expand_dims(inputs, -1)
merge_net = tflearn.conv_2d(
_input, FEATURE_NUM, KERNEL, activation='relu')
merge_net = tflearn.conv_2d(
merge_net, FEATURE_NUM, KERNEL, activation='relu')
avg_net = tflearn.global_avg_pool(merge_net)
# dense_net_0 = tflearn.fully_connected(
# merge_net, 64, activation='relu')
#dense_net_0 = tflearn.dropout(dense_net_0, 0.8)
out = tflearn.fully_connected(avg_net, 1, activation='linear')
return inputs, out
Example #3
| Source File: vqn.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def vqn_model(x):
with tf.variable_scope('vqn'):
inputs = tflearn.input_data(placeholder=x)
_split_array = []
for i in range(INPUT_SEQ):
tmp_network = tf.reshape(
inputs[:, i:i+1, :, :, :], [-1, INPUT_H, INPUT_W, INPUT_D])
if i == 0:
_split_array.append(CNN_Core(tmp_network))
else:
_split_array.append(CNN_Core(tmp_network,True))
merge_net = tflearn.merge(_split_array, 'concat')
merge_net = tflearn.flatten(merge_net)
_count = merge_net.get_shape().as_list()[1]
with tf.variable_scope('full-cnn'):
net = tf.reshape(merge_net, [-1, _count / DENSE_SIZE, DENSE_SIZE, 1])
out = vgg16(net, OUTPUT_DIM)
return out
Example #4
| Source File: cnn.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def vqn_model(x):
with tf.variable_scope('vqn'):
inputs = tflearn.input_data(placeholder=x)
_split_array = []
for i in range(INPUT_SEQ):
tmp_network = tf.reshape(
inputs[:, i:i+1, :, :, :], [-1, INPUT_H, INPUT_W, INPUT_D])
if i == 0:
_split_array.append(CNN_Core(tmp_network))
else:
_split_array.append(CNN_Core(tmp_network, True))
merge_net = tflearn.merge(_split_array, 'concat')
merge_net = tflearn.flatten(merge_net)
_count = merge_net.get_shape().as_list()[1]
with tf.variable_scope('full-cnn'):
net = tf.reshape(
merge_net, [-1, _count / DENSE_SIZE, DENSE_SIZE, 1])
out = vgg16(net, OUTPUT_DIM)
return out
Example #5
| Source File: inet.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def create_network(self):
with tf.variable_scope('innovation'):
inputs = tflearn.input_data(shape=[None, self.s_dim[0], self.s_dim[1]])
split_array = []
for i in xrange(self.s_dim[0] - 1):
split = tflearn.conv_1d(inputs[:, i:i + 1, :], FEATURE_NUM, KERNEL, activation='relu')
flattern = tflearn.flatten(split)
split_array.append(flattern)
#dense_net= tflearn.fully_connected(inputs[:, -1:, 0:5], FEATURE_NUM, activation='relu')
split_array.append(inputs[:, -1, 0:5])
merge_net = tflearn.merge(split_array, 'concat')
dense_net_0 = tflearn.fully_connected(merge_net, 64, activation='relu')
out = tflearn.fully_connected(dense_net_0, self.a_dim, activation='softmax')
return inputs, out
Example #6
| Source File: models.py From pygta5 with GNU General Public License v3.0 | 6 votes |
|
def resnext(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
net = input_data(shape=[None, width, height, 3], name='input')
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.layers.conv.resnext_block(net, n, 16, 32)
net = tflearn.resnext_block(net, 1, 32, 32, downsample=True)
net = tflearn.resnext_block(net, n-1, 32, 32)
net = tflearn.resnext_block(net, 1, 64, 32, downsample=True)
net = tflearn.resnext_block(net, n-1, 64, 32)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, output, activation='softmax')
opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=opt,
loss='categorical_crossentropy')
model = tflearn.DNN(net,
max_checkpoints=0, tensorboard_verbose=0, tensorboard_dir='log')
return model
Example #7
| Source File: a3c.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def create_actor_network(self):
with tf.variable_scope('actor'):
inputs = tflearn.input_data(shape=[None, self.s_dim[0], self.s_dim[1]])
split_array = []
for i in xrange(self.s_dim[0] - 1):
split = tflearn.conv_1d(inputs[:, i:i + 1, :], FEATURE_NUM, KERNEL, activation='relu')
flattern = tflearn.flatten(split)
split_array.append(flattern)
dense_net= tflearn.fully_connected(inputs[:, -1:, :], FEATURE_NUM, activation='relu')
split_array.append(dense_net)
merge_net = tflearn.merge(split_array, 'concat')
dense_net_0 = tflearn.fully_connected(merge_net, 64, activation='relu')
# dense_net_0 = tflearn.dropout(dense_net_0, 0.8)
out = tflearn.fully_connected(dense_net_0, self.a_dim, activation='softmax')
return inputs, out
Example #8
| Source File: a3c.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def create_critic_network(self):
with tf.variable_scope('critic'):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]])
_input = tf.expand_dims(inputs, -1)
merge_net = tflearn.conv_2d(
_input, FEATURE_NUM, KERNEL, activation='relu')
merge_net = tflearn.conv_2d(
merge_net, FEATURE_NUM, KERNEL, activation='relu')
avg_net = tflearn.global_avg_pool(merge_net)
# dense_net_0 = tflearn.fully_connected(
# merge_net, 64, activation='relu')
#dense_net_0 = tflearn.dropout(dense_net_0, 0.8)
out = tflearn.fully_connected(avg_net, 1, activation='linear')
return inputs, out
Example #9
| Source File: models.py From pygta5 with GNU General Public License v3.0 | 6 votes |
|
def resnext(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
net = input_data(shape=[None, width, height, 3], name='input')
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.layers.conv.resnext_block(net, n, 16, 32)
net = tflearn.resnext_block(net, 1, 32, 32, downsample=True)
net = tflearn.resnext_block(net, n-1, 32, 32)
net = tflearn.resnext_block(net, 1, 64, 32, downsample=True)
net = tflearn.resnext_block(net, n-1, 64, 32)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, output, activation='softmax')
opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=opt,
loss='categorical_crossentropy')
model = tflearn.DNN(net,
max_checkpoints=0, tensorboard_verbose=0, tensorboard_dir='log')
return model
Example #10
| Source File: a3c.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def create_actor_network(self):
with tf.variable_scope('actor'):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]])
_input = tf.expand_dims(inputs, -1)
merge_net = tflearn.conv_2d(
_input, FEATURE_NUM, KERNEL, activation='relu')
merge_net = tflearn.conv_2d(
merge_net, FEATURE_NUM, KERNEL, activation='relu')
avg_net = tflearn.global_avg_pool(merge_net)
out = tflearn.fully_connected(
avg_net, self.a_dim, activation='softmax')
return inputs, out
Example #11
| Source File: cnn.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def vqn_model(x):
with tf.variable_scope('vqn'):
inputs = tflearn.input_data(placeholder=x)
_split_array = []
for i in range(INPUT_SEQ):
tmp_network = tf.reshape(
inputs[:, i:i+1, :, :, :], [-1, INPUT_H, INPUT_W, INPUT_D])
if i == 0:
_split_array.append(CNN_Core(tmp_network))
else:
_split_array.append(CNN_Core(tmp_network, True))
merge_net = tflearn.merge(_split_array, 'concat')
merge_net = tflearn.flatten(merge_net)
_count = merge_net.get_shape().as_list()[1]
with tf.variable_scope('full-cnn'):
net = tf.reshape(
merge_net, [-1, _count / DENSE_SIZE, DENSE_SIZE, 1])
out = vgg16(net, OUTPUT_DIM)
return out
Example #12
| Source File: test_layers.py From FRU with MIT License | 6 votes |
|
def test_feed_dict_no_None(self):
X = [[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 1., 0.], [1., 1., 1., 0.]]
Y = [[1., 0.], [0., 1.], [1., 0.], [0., 1.]]
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 4], name="X_in")
g = tflearn.reshape(g, new_shape=[-1, 2, 2, 1])
g = tflearn.conv_2d(g, 4, 2)
g = tflearn.conv_2d(g, 4, 1)
g = tflearn.max_pool_2d(g, 2)
g = tflearn.fully_connected(g, 2, activation='softmax')
g = tflearn.regression(g, optimizer='sgd', learning_rate=1.)
m = tflearn.DNN(g)
def do_fit():
m.fit({"X_in": X, 'non_existent': X}, Y, n_epoch=30, snapshot_epoch=False)
self.assertRaisesRegexp(Exception, "Feed dict asks for variable named 'non_existent' but no such variable is known to exist", do_fit)
Example #13
| Source File: models.py From pygta5 with GNU General Public License v3.0 | 6 votes |
|
def resnext(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
net = input_data(shape=[None, width, height, 3], name='input')
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.layers.conv.resnext_block(net, n, 16, 32)
net = tflearn.resnext_block(net, 1, 32, 32, downsample=True)
net = tflearn.resnext_block(net, n-1, 32, 32)
net = tflearn.resnext_block(net, 1, 64, 32, downsample=True)
net = tflearn.resnext_block(net, n-1, 64, 32)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, output, activation='softmax')
opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=opt,
loss='categorical_crossentropy')
model = tflearn.DNN(net,
max_checkpoints=0, tensorboard_verbose=0, tensorboard_dir='log')
return model
Example #14
| Source File: Model.py From NLU with MIT License | 6 votes |
|
def createDNNLayers(self, x, y):
###############################################################
#
# Sets up the DNN layers, configuration in required/confs.json
#
###############################################################
net = tflearn.input_data(shape=[None, len(x[0])])
for i in range(self._confs["NLU"]['FcLayers']):
net = tflearn.fully_connected(net, self._confs["NLU"]['FcUnits'])
net = tflearn.fully_connected(net, len(y[0]), activation=str(self._confs["NLU"]['Activation']))
if self._confs["NLU"]['Regression']:
net = tflearn.regression(net)
return net
Example #15
| Source File: my_seq2seq.py From ChatBotCourse with MIT License | 5 votes |
|
def model(self, feed_previous=False):
# 通过输入的XY生成encoder_inputs和带GO头的decoder_inputs
input_data = tflearn.input_data(shape=[None, self.max_seq_len*2, self.word_vec_dim], dtype=tf.float32, name = "XY")
encoder_inputs = tf.slice(input_data, [0, 0, 0], [-1, self.max_seq_len, self.word_vec_dim], name="enc_in")
decoder_inputs_tmp = tf.slice(input_data, [0, self.max_seq_len, 0], [-1, self.max_seq_len-1, self.word_vec_dim], name="dec_in_tmp")
go_inputs = tf.ones_like(decoder_inputs_tmp)
go_inputs = tf.slice(go_inputs, [0, 0, 0], [-1, 1, self.word_vec_dim])
decoder_inputs = tf.concat(1, [go_inputs, decoder_inputs_tmp], name="dec_in")
# 编码器
# 把encoder_inputs交给编码器，返回一个输出(预测序列的第一个值)和一个状态(传给解码器)
(encoder_output_tensor, states) = tflearn.lstm(encoder_inputs, self.word_vec_dim, return_state=True, scope='encoder_lstm')
encoder_output_sequence = tf.pack([encoder_output_tensor], axis=1)
# 解码器
# 预测过程用前一个时间序的输出作为下一个时间序的输入
# 先用编码器的最后一个输出作为第一个输入
if feed_previous:
first_dec_input = go_inputs
else:
first_dec_input = tf.slice(decoder_inputs, [0, 0, 0], [-1, 1, self.word_vec_dim])
decoder_output_tensor = tflearn.lstm(first_dec_input, self.word_vec_dim, initial_state=states, return_seq=False, reuse=False, scope='decoder_lstm')
decoder_output_sequence_single = tf.pack([decoder_output_tensor], axis=1)
decoder_output_sequence_list = [decoder_output_tensor]
# 再用解码器的输出作为下一个时序的输入
for i in range(self.max_seq_len-1):
if feed_previous:
next_dec_input = decoder_output_sequence_single
else:
next_dec_input = tf.slice(decoder_inputs, [0, i+1, 0], [-1, 1, self.word_vec_dim])
decoder_output_tensor = tflearn.lstm(next_dec_input, self.word_vec_dim, return_seq=False, reuse=True, scope='decoder_lstm')
decoder_output_sequence_single = tf.pack([decoder_output_tensor], axis=1)
decoder_output_sequence_list.append(decoder_output_tensor)
decoder_output_sequence = tf.pack(decoder_output_sequence_list, axis=1)
real_output_sequence = tf.concat(1, [encoder_output_sequence, decoder_output_sequence])
net = tflearn.regression(real_output_sequence, optimizer='sgd', learning_rate=0.1, loss='mean_square')
model = tflearn.DNN(net)
return model
Example #16
| Source File: test_models_loading_scope.py From FRU with MIT License | 5 votes |
|
def test_dnn_loading_scope(self):
with tf.Graph().as_default():
X = [3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1]
Y = [1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3]
input = tflearn.input_data(shape=[None])
linear = tflearn.single_unit(input)
regression = tflearn.regression(linear, optimizer='sgd', loss='mean_square',
metric='R2', learning_rate=0.01)
m = tflearn.DNN(regression)
# Testing fit and predict
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
res = m.predict([3.2])[0]
self.assertGreater(res, 1.3, "DNN test (linear regression) failed! with score: " + str(res) + " expected > 1.3")
self.assertLess(res, 1.8, "DNN test (linear regression) failed! with score: " + str(res) + " expected < 1.8")
# Testing save method
m.save("test_dnn.tflearn")
self.assertTrue(os.path.exists("test_dnn.tflearn.index"))
# Testing loading, with change of variable scope (saved with no scope, now loading into scopeA)
with tf.Graph().as_default(): # start with clear graph
with tf.variable_scope("scopeA") as scope:
input = tflearn.input_data(shape=[None])
linear = tflearn.single_unit(input)
regression = tflearn.regression(linear, optimizer='sgd', loss='mean_square',
metric='R2', learning_rate=0.01)
m = tflearn.DNN(regression)
def try_load():
m.load("test_dnn.tflearn")
self.assertRaises(tf.errors.NotFoundError, try_load) # fails, since names in file don't have "scopeA"
m.load("test_dnn.tflearn", variable_name_map=("scopeA/", "")) # succeeds, because variable names are rewritten
res = m.predict([3.2])[0]
self.assertGreater(res, 1.3, "DNN test (linear regression) failed after loading model! score: " + str(res) + " expected > 1.3")
self.assertLess(res, 1.8, "DNN test (linear regression) failed after loading model! score: " + str(res) + " expected < 1.8")
Example #17
| Source File: test_layers.py From FRU with MIT License | 5 votes |
|
def test_conv_layers(self):
X = [[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 1., 0.], [1., 1., 1., 0.]]
Y = [[1., 0.], [0., 1.], [1., 0.], [0., 1.]]
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 4])
g = tflearn.reshape(g, new_shape=[-1, 2, 2, 1])
g = tflearn.conv_2d(g, 4, 2, activation='relu')
g = tflearn.max_pool_2d(g, 2)
g = tflearn.fully_connected(g, 2, activation='softmax')
g = tflearn.regression(g, optimizer='sgd', learning_rate=1.)
m = tflearn.DNN(g)
m.fit(X, Y, n_epoch=100, snapshot_epoch=False)
# TODO: Fix test
#self.assertGreater(m.predict([[1., 0., 0., 0.]])[0][0], 0.5)
# Bulk Tests
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 4])
g = tflearn.reshape(g, new_shape=[-1, 2, 2, 1])
g = tflearn.conv_2d(g, 4, 2)
g = tflearn.conv_2d(g, 4, 1)
g = tflearn.conv_2d_transpose(g, 4, 2, [2, 2])
g = tflearn.max_pool_2d(g, 2)
Example #18
| Source File: test_layers.py From FRU with MIT License | 5 votes |
|
def test_recurrent_layers(self):
X = [[1, 3, 5, 7], [2, 4, 8, 10], [1, 5, 9, 11], [2, 6, 8, 0]]
Y = [[0., 1.], [1., 0.], [0., 1.], [1., 0.]]
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 4])
g = tflearn.embedding(g, input_dim=12, output_dim=4)
g = tflearn.lstm(g, 6)
g = tflearn.fully_connected(g, 2, activation='softmax')
g = tflearn.regression(g, optimizer='sgd', learning_rate=1.)
m = tflearn.DNN(g)
m.fit(X, Y, n_epoch=300, snapshot_epoch=False)
self.assertGreater(m.predict([[5, 9, 11, 1]])[0][1], 0.9)
Example #19
| Source File: test_layers.py From FRU with MIT License | 5 votes |
|
def test_regression_placeholder(self):
'''
Check that regression does not duplicate placeholders
'''
with tf.Graph().as_default():
g = tflearn.input_data(shape=[None, 2])
g_nand = tflearn.fully_connected(g, 1, activation='linear')
with tf.name_scope("Y"):
Y_in = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="Y")
tflearn.regression(g_nand, optimizer='sgd',
placeholder=Y_in,
learning_rate=2.,
loss='binary_crossentropy',
op_name="regression1",
name="Y")
# for this test, just use the same default trainable_vars
# in practice, this should be different for the two regressions
tflearn.regression(g_nand, optimizer='adam',
placeholder=Y_in,
learning_rate=2.,
loss='binary_crossentropy',
op_name="regression2",
name="Y")
self.assertEqual(len(tf.get_collection(tf.GraphKeys.TARGETS)), 1)
Example #20
| Source File: test_models.py From FRU with MIT License | 5 votes |
|
def test_sequencegenerator(self):
with tf.Graph().as_default():
text = "123456789101234567891012345678910123456789101234567891012345678910"
maxlen = 5
X, Y, char_idx = \
tflearn.data_utils.string_to_semi_redundant_sequences(text, seq_maxlen=maxlen, redun_step=3)
g = tflearn.input_data(shape=[None, maxlen, len(char_idx)])
g = tflearn.lstm(g, 32)
g = tflearn.dropout(g, 0.5)
g = tflearn.fully_connected(g, len(char_idx), activation='softmax')
g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy',
learning_rate=0.1)
m = tflearn.SequenceGenerator(g, dictionary=char_idx,
seq_maxlen=maxlen,
clip_gradients=5.0)
m.fit(X, Y, validation_set=0.1, n_epoch=100, snapshot_epoch=False)
res = m.generate(10, temperature=.5, seq_seed="12345")
#self.assertEqual(res, "123456789101234", "SequenceGenerator test failed! Generated sequence: " + res + " expected '123456789101234'")
# Testing save method
m.save("test_seqgen.tflearn")
self.assertTrue(os.path.exists("test_seqgen.tflearn.index"))
# Testing load method
m.load("test_seqgen.tflearn")
res = m.generate(10, temperature=.5, seq_seed="12345")
# TODO: Fix test
#self.assertEqual(res, "123456789101234", "SequenceGenerator test failed after loading model! Generated sequence: " + res + " expected '123456789101234'")
Example #21
| Source File: models.py From pygta5 with GNU General Public License v3.0 | 5 votes |
|
def sentnet_frames(width, height, frame_count, lr, output=9):
network = input_data(shape=[None, width, height,frame_count, 1], name='input')
network = conv_3d(network, 96, 11, strides=4, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 256, 5, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 256, 3, activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = conv_3d(network, 256, 5, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 256, 3, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, output, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
Example #22
| Source File: qarc.py From QARC with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def vqn_model(self, x):
with tf.variable_scope('vqn'):
inputs = tflearn.input_data(placeholder=x)
_split_array = []
for i in range(INPUT_SEQ):
tmp_network = tf.reshape(
inputs[:, i:i+1, :, :, :], [-1, INPUT_H, INPUT_W, INPUT_D])
if i == 0:
_split_array.append(self.CNN_Core(tmp_network))
else:
_split_array.append(self.CNN_Core(tmp_network, True))
merge_net = tflearn.merge(_split_array, 'concat')
merge_net = tflearn.flatten(merge_net)
_count = merge_net.get_shape().as_list()[1]
with tf.variable_scope('full-cnn'):
net = tf.reshape(
merge_net, [-1, INPUT_SEQ, _count / INPUT_SEQ, 1])
network = tflearn.conv_2d(
net, KERNEL, 5, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 3)
network = tflearn.layers.normalization.batch_normalization(
network)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.layers.normalization.batch_normalization(
network)
cnn_result = tflearn.fully_connected(
network, DENSE_SIZE, activation='relu')
out = tflearn.fully_connected(
cnn_result, OUTPUT_DIM, activation='sigmoid')
return out
Example #23
| Source File: gray.py From QARC with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def vqn_model(x):
with tf.variable_scope('vqn'):
inputs = tflearn.input_data(placeholder=x)
_split_array = []
_cnn_array = []
for i in range(INPUT_SEQ):
tmp_network = tf.reshape(
inputs[:, i:i+1, :, :, :], [-1, INPUT_H, INPUT_W, INPUT_D])
if i == 0:
_tmp_split, _tmp_cnn = CNN_Core(tmp_network)
else:
_tmp_split, _tmp_cnn = CNN_Core(tmp_network, True)
_split_array.append(_tmp_split)
_cnn_array.append(_tmp_cnn)
merge_net = tflearn.merge(_split_array, 'concat')
merge_net = tflearn.flatten(merge_net)
_count = merge_net.get_shape().as_list()[1]
with tf.variable_scope('full-lstm'):
net = tf.reshape(merge_net, [-1, INPUT_SEQ, _count / INPUT_SEQ])
net = tflearn.gru(net, HIDDEN_UNIT, return_seq=True)
net = tflearn.gru(net, HIDDEN_UNIT, return_seq=True)
net, alphas = attention(net, HIDDEN_UNIT)
out = tflearn.fully_connected(
net, OUTPUT_DIM, activation='sigmoid')
return out, tf.stack(_cnn_array, axis=0), alphas
Example #24
| Source File: sentiment.py From TaobaoAnalysis with MIT License | 5 votes |
|
def _create_model(self):
reset_default_graph()
net = input_data([None, SEQUENCE_LEN])
net = embedding(net, input_dim=len(self._vocab.vocabulary_),
output_dim=WORD_FEATURE_DIM)
net = lstm(net, DOC_FEATURE_DIM, dropout=0.8)
net = fully_connected(net, 2, activation='softmax')
net = regression(net, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy')
return DNN(net)
Example #25
| Source File: usefulness.py From TaobaoAnalysis with MIT License | 5 votes |
|
def _create_model():
reset_default_graph()
net = input_data([None, 5])
net = fully_connected(net, N_HIDDEN_UNITS, bias=True, activation='tanh')
net = fully_connected(net, 2, activation='softmax')
net = regression(net, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy')
return DNN(net)
Example #26
| Source File: models.py From pygta5 with GNU General Public License v3.0 | 5 votes |
|
def alexnet(width, height, lr, output=3):
network = input_data(shape=[None, width, height, 1], name='input')
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, output, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
Example #27
| Source File: models.py From pygta5 with GNU General Public License v3.0 | 5 votes |
|
def sentnet_v0(width, height, frame_count, lr, output=9):
network = input_data(shape=[None, width, height, frame_count, 1], name='input')
network = conv_3d(network, 96, 11, strides=4, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 256, 5, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 384, 3, 3, activation='relu')
network = conv_3d(network, 384, 3, 3, activation='relu')
network = conv_3d(network, 256, 3, 3, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, output, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
Example #28
| Source File: models.py From pygta5 with GNU General Public License v3.0 | 5 votes |
|
def alexnet2(width, height, lr, output=3):
network = input_data(shape=[None, width, height, 1], name='input')
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, output, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
Example #29
| Source File: models.py From pygta5 with GNU General Public License v3.0 | 5 votes |
|
def sentnet(width, height, frame_count, lr, output=9):
network = input_data(shape=[None, width, height, frame_count, 1], name='input')
network = conv_3d(network, 96, 11, strides=4, activation='relu')
network = avg_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 256, 5, activation='relu')
network = avg_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 256, 3, activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = conv_3d(network, 256, 5, activation='relu')
network = avg_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 256, 3, activation='relu')
network = avg_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, output, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
Example #30
| Source File: models.py From pygta5 with GNU General Public License v3.0 | 5 votes |
|
def sentnet_color(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
network = input_data(shape=[None, width, height,3, 1], name='input')
network = conv_3d(network, 96, 11, strides=4, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 256, 5, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 256, 3, activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = conv_3d(network, 256, 5, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 384, 3, activation='relu')
network = conv_3d(network, 256, 3, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, output, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path=model_name,
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
