Python tflearn.fully_connected() Examples
The following are 30
code examples of tflearn.fully_connected().
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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: cnn.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def CNN_Core(x, reuse=False):
with tf.variable_scope('cnn_core', reuse=reuse):
network = tflearn.conv_2d(
x, KERNEL, 5, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL // 2, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
# network = tflearn.fully_connected(
# network, DENSE_SIZE, activation='relu')
split_flat = tflearn.flatten(network)
return split_flat
Example #3
| 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 #4
| Source File: cnn.py From QARC with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def CNN_Core(x, reuse=False):
with tf.variable_scope('cnn_core', reuse=reuse):
network = tflearn.conv_2d(
x, KERNEL, 5, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.max_pool_2d(network, 2)
network = tflearn.conv_2d(
network, KERNEL // 2, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
# network = tflearn.fully_connected(
# network, DENSE_SIZE, activation='relu')
split_flat = tflearn.flatten(network)
return split_flat
Example #5
| 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 #6
| 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 #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]])
_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 #8
| 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 #9
| 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]])
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, 1, activation='linear')
return inputs, out
Example #10
| 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 #11
| 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 #12
| 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 #13
| Source File: dcgan.py From FRU with MIT License | 5 votes |
|
def generator(x, reuse=False):
with tf.variable_scope('Generator', reuse=reuse):
x = tflearn.fully_connected(x, n_units=7 * 7 * 128)
x = tflearn.batch_normalization(x)
x = tf.nn.tanh(x)
x = tf.reshape(x, shape=[-1, 7, 7, 128])
x = tflearn.upsample_2d(x, 2)
x = tflearn.conv_2d(x, 64, 5, activation='tanh')
x = tflearn.upsample_2d(x, 2)
x = tflearn.conv_2d(x, 1, 5, activation='sigmoid')
return x
# Discriminator
Example #14
| Source File: gan.py From FRU with MIT License | 5 votes |
|
def generator(x, reuse=False):
with tf.variable_scope('Generator', reuse=reuse):
x = tflearn.fully_connected(x, 256, activation='relu')
x = tflearn.fully_connected(x, image_dim, activation='sigmoid')
return x
# Discriminator
Example #15
| Source File: vqn.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 = []
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-lstm'):
net = tf.reshape(merge_net, [-1, _count / DENSE_SIZE, DENSE_SIZE])
net = tflearn.gru(net, DENSE_SIZE, return_seq=True)
out_gru = tflearn.gru(net, DENSE_SIZE,dropout=0.8)
gru_result = tflearn.fully_connected(out_gru, DENSE_SIZE, activation='relu')
out = tflearn.fully_connected(
gru_result, OUTPUT_DIM, activation='sigmoid')
return out
Example #16
| Source File: manager.py From Fruit-API with GNU General Public License v3.0 | 5 votes |
|
def create_output(self, input_data, output_size, activation_fn='linear', scope=None):
output = tflearn.fully_connected(input_data, output_size, activation=activation_fn, scope=scope)
self.outputs.append(output)
self.num_of_outputs += 1
return output
# input_data must be [batch_size, data_size]
Example #17
| Source File: weights_loading_scope.py From FRU with MIT License | 5 votes |
|
def make_core_network(network):
network = tflearn.reshape(network, [-1, 28, 28, 1], name="reshape")
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 10, activation='softmax')
return network
Example #18
| Source File: weights_loading_scope.py From FRU with MIT License | 5 votes |
|
def make_core_network(network):
dense1 = tflearn.fully_connected(network, 64, activation='tanh',
regularizer='L2', weight_decay=0.001, name="dense1")
dropout1 = tflearn.dropout(dense1, 0.8)
dense2 = tflearn.fully_connected(dropout1, 64, activation='tanh',
regularizer='L2', weight_decay=0.001, name="dense2")
dropout2 = tflearn.dropout(dense2, 0.8)
softmax = tflearn.fully_connected(dropout2, 10, activation='softmax', name="softmax")
return softmax
Example #19
| Source File: weights_loading_scope.py From FRU with MIT License | 5 votes |
|
def __init__(self):
inputs = tflearn.input_data(shape=[None, 784], name="input")
with tf.variable_scope("scope1") as scope:
net_conv = Model1.make_core_network(inputs) # shape (?, 10)
with tf.variable_scope("scope2") as scope:
net_dnn = Model2.make_core_network(inputs) # shape (?, 10)
network = tf.concat([net_conv, net_dnn], 1, name="concat") # shape (?, 20)
network = tflearn.fully_connected(network, 10, activation="softmax")
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
self.model = tflearn.DNN(network, tensorboard_verbose=0)
Example #20
| Source File: vqn.py From QARC with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def CNN_Core(x,reuse=False):
with tf.variable_scope('cnn_core',reuse=reuse):
network = tflearn.conv_2d(
x, KERNEL, 3, activation='relu', regularizer="L2",weight_decay=0.0001)
network = tflearn.avg_pool_2d(network, 3)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2",weight_decay=0.0001)
network = tflearn.avg_pool_2d(network, 2)
network = tflearn.fully_connected(
network, DENSE_SIZE, activation='relu')
split_flat = tflearn.flatten(network)
return split_flat
Example #21
| Source File: vqpn.py From QARC with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def CNN_Core(x,reuse=False):
with tf.variable_scope('cnn_core',reuse=reuse):
network = tflearn.conv_2d(
x, KERNEL, 3, activation='relu', regularizer="L2",weight_decay=0.0001)
network = tflearn.avg_pool_2d(network, 3)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2",weight_decay=0.0001)
network = tflearn.avg_pool_2d(network, 2)
network = tflearn.fully_connected(
network, DENSE_SIZE, activation='relu')
split_flat = tflearn.flatten(network)
return split_flat
Example #22
| Source File: vqn-new.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 = []
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-lstm'):
net = tf.reshape(merge_net, [-1, INPUT_SEQ, _count / INPUT_SEQ])
net = tflearn.gru(net, DENSE_SIZE, return_seq=True)
out_gru = tflearn.gru(net, DENSE_SIZE,dropout=0.8)
gru_result = tflearn.fully_connected(out_gru, DENSE_SIZE, activation='relu')
out = tflearn.fully_connected(
gru_result, OUTPUT_DIM, activation='sigmoid')
return out
Example #23
| 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 #24
| Source File: qarc.py From QARC with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def CNN_Core(self, x, reuse=False):
with tf.variable_scope('cnn_core', reuse=reuse):
network = tflearn.conv_2d(
x, KERNEL, 5, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.avg_pool_2d(network, 3)
network = tflearn.conv_2d(
network, KERNEL, 3, activation='relu', regularizer="L2", weight_decay=0.0001)
network = tflearn.avg_pool_2d(network, 2)
network = tflearn.fully_connected(
network, DENSE_SIZE, activation='relu')
split_flat = tflearn.flatten(network)
return split_flat
Example #25
| 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 #26
| 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 #27
| 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 #28
| 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 #29
| 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 #30
| 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
