本文整理汇总了Python中tflearn.input_data方法的典型用法代码示例。如果您正苦于以下问题:Python tflearn.input_data方法的具体用法?Python tflearn.input_data怎么用?Python tflearn.input_data使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tflearn
的用法示例。
在下文中一共展示了tflearn.input_data方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: resnext
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
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
示例2: create_actor_network
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
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
示例3: create_critic_network
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
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
示例4: create_network
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
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
示例5: vqn_model
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
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
示例6: vqn_model
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
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
示例7: create_actor_network
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
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
示例8: test_feed_dict_no_None
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
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)
示例9: createDNNLayers
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
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
示例10: build_estimator
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
def build_estimator(model_dir, model_type, embeddings,index_map, combination_method):
"""Build an estimator."""
# Continuous base columns.
node1 = tf.contrib.layers.real_valued_column("node1")
deep_columns = [node1]
if model_type == "regressor":
tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5)
if combination_method == 'concatenate':
net = tflearn.input_data(shape=[None, embeddings.shape[1]*2])
else:
net = tflearn.input_data(shape=[None, embeddings.shape[1]] )
net = tflearn.fully_connected(net, 100, activation='relu')
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy')
m = tflearn.DNN(net)
else:
m = tf.contrib.learn.DNNLinearCombinedClassifier(
model_dir=model_dir,
linear_feature_columns=wide_columns,
dnn_feature_columns=deep_columns,
dnn_hidden_units=[100])
return m
示例11: get_network
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
def get_network(frames, input_size, num_classes):
"""Create our LSTM"""
net = tflearn.input_data(shape=[None, frames, input_size])
net = tflearn.lstm(net, 128, dropout=0.8, return_seq=True)
net = tflearn.lstm(net, 128)
net = tflearn.fully_connected(net, num_classes, activation='softmax')
net = tflearn.regression(net, optimizer='adam',
loss='categorical_crossentropy', name="output1")
return net
示例12: get_network_deep
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
def get_network_deep(frames, input_size, num_classes):
"""Create a deeper LSTM"""
net = tflearn.input_data(shape=[None, frames, input_size])
net = tflearn.lstm(net, 64, dropout=0.2, return_seq=True)
net = tflearn.lstm(net, 64, dropout=0.2, return_seq=True)
net = tflearn.lstm(net, 64, dropout=0.2)
net = tflearn.fully_connected(net, num_classes, activation='softmax')
net = tflearn.regression(net, optimizer='adam',
loss='categorical_crossentropy', name="output1")
return net
示例13: get_network_wide
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
def get_network_wide(frames, input_size, num_classes):
"""Create a wider LSTM"""
net = tflearn.input_data(shape=[None, frames, input_size])
net = tflearn.lstm(net, 256, dropout=0.2)
net = tflearn.fully_connected(net, num_classes, activation='softmax')
net = tflearn.regression(net, optimizer='adam',
loss='categorical_crossentropy', name='output1')
return net
示例14: get_network_wider
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
def get_network_wider(frames, input_size, num_classes):
"""Create a wider LSTM"""
net = tflearn.input_data(shape=[None, frames, input_size])
net = tflearn.lstm(net, 512, dropout=0.2)
net = tflearn.fully_connected(net, num_classes, activation='softmax')
net = tflearn.regression(net, optimizer='adam',
loss='categorical_crossentropy', name='output1')
return net
示例15: vgg_net_19
# 需要导入模块: import tflearn [as 别名]
# 或者: from tflearn import input_data [as 别名]
def vgg_net_19(width, height):
network = input_data(shape=[None, height, width, 3], name='input')
network = conv_2d(network, 64, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 64, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 128, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 128, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 256, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = conv_2d(network, 512, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network, 2, strides=2)
network = fully_connected(network, 4096, activation='relu', weight_decay=5e-4)
network = dropout(network, keep_prob=0.5)
network = fully_connected(network, 4096, activation='relu', weight_decay=5e-4)
network = dropout(network, keep_prob=0.5)
network = fully_connected(network, 1000, activation='softmax', weight_decay=5e-4)
opt = Momentum(learning_rate=0, momentum = 0.9)
network = regression(network, optimizer=opt, loss='categorical_crossentropy', name='targets')
model = DNN(network, checkpoint_path='', max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='')
return model
#model of vgg-19 for testing of the activations
#rename the output you want to test, connect it to the next layer and change the output layer at the bottom (model = DNN(...))
#make sure to use the correct test function (depending if your output is a tensor or a vector)