Python tflearn.dropout() Examples
The following are 15
code examples of tflearn.dropout().
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Example #1
| 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 #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: 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 #4
| 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 #5
| Source File: model.py From tensorflow2caffe with MIT License | 5 votes |
|
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)
Example #6
| Source File: model.py From tensorflow2caffe with MIT License | 5 votes |
|
def vgg_net_19_activations(width, height):
network = input_data(shape=[None, height, width, 3], name='input')
network1 = conv_2d(network, 64, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network2 = conv_2d(network1, 64, 3, activation = 'relu', regularizer='L2', weight_decay=5e-4)
network = max_pool_2d(network2, 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(network1, checkpoint_path='', max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='')
return model
Example #7
| Source File: vqn.py From QARC with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def vgg16(input, num_class):
x = tflearn.conv_2d(input, 64, 3, activation='relu', scope='conv1_1')
x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_1')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool3')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool4')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool5')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc6')
x = tflearn.dropout(x, 0.5, name='dropout1')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc7')
x = tflearn.dropout(x, 0.5, name='dropout2')
x = tflearn.fully_connected(
x, num_class, activation='sigmoid', scope='fc8', restore=False)
return x
Example #8
| Source File: vgg16.py From models with MIT License | 5 votes |
|
def vgg16(placeholderX=None):
x = tflearn.input_data(shape=[None, 224, 224, 3], name='input',
placeholder=placeholderX)
x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_1')
x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_1')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool3')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool4')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool5')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc6')
x = tflearn.dropout(x, 0.5, name='dropout1')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc7')
x = tflearn.dropout(x, 0.5, name='dropout2')
x = tflearn.fully_connected(x, 1000, activation='softmax', scope='fc8')
return x
Example #9
| 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 #10
| 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 #11
| Source File: vgg_network_finetuning.py From FRU with MIT License | 5 votes |
|
def vgg16(input, num_class):
x = tflearn.conv_2d(input, 64, 3, activation='relu', scope='conv1_1')
x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_1')
x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_2')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_1')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_2')
x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool3')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool4')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_1')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_2')
x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_3')
x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool5')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc6')
x = tflearn.dropout(x, 0.5, name='dropout1')
x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc7')
x = tflearn.dropout(x, 0.5, name='dropout2')
x = tflearn.fully_connected(x, num_class, activation='softmax', scope='fc8',
restore=False)
return x
Example #12
| Source File: recommender_wide_and_deep.py From FRU with MIT License | 5 votes |
|
def deep_model(self, wide_inputs, n_inputs, n_nodes=[100, 50], use_dropout=False):
'''
Model - deep, i.e. two-layer fully connected network model
'''
cc_input_var = {}
cc_embed_var = {}
flat_vars = []
if self.verbose:
print ("--> deep model: %s categories, %d continuous" % (len(self.categorical_columns), n_inputs))
for cc, cc_size in self.categorical_columns.items():
cc_input_var[cc] = tflearn.input_data(shape=[None, 1], name="%s_in" % cc, dtype=tf.int32)
# embedding layers only work on CPU! No GPU implementation in tensorflow, yet!
cc_embed_var[cc] = tflearn.layers.embedding_ops.embedding(cc_input_var[cc], cc_size, 8, name="deep_%s_embed" % cc)
if self.verbose:
print (" %s_embed = %s" % (cc, cc_embed_var[cc]))
flat_vars.append(tf.squeeze(cc_embed_var[cc], squeeze_dims=[1], name="%s_squeeze" % cc))
network = tf.concat([wide_inputs] + flat_vars, 1, name="deep_concat")
for k in range(len(n_nodes)):
network = tflearn.fully_connected(network, n_nodes[k], activation="relu", name="deep_fc%d" % (k+1))
if use_dropout:
network = tflearn.dropout(network, 0.5, name="deep_dropout%d" % (k+1))
if self.verbose:
print ("Deep model network before output %s" % network)
network = tflearn.fully_connected(network, 1, activation="linear", name="deep_fc_output", bias=False)
network = tf.reshape(network, [-1, 1]) # so that accuracy is binary_accuracy
if self.verbose:
print ("Deep model network %s" % network)
return network
Example #13
| Source File: tflearn_wide_and_deep.py From tflearn_wide_and_deep with MIT License | 5 votes |
|
def deep_model(self, wide_inputs, n_inputs, n_nodes=[100, 50], use_dropout=False):
'''
Model - deep, i.e. two-layer fully connected network model
'''
cc_input_var = {}
cc_embed_var = {}
flat_vars = []
if self.verbose:
print ("--> deep model: %s categories, %d continuous" % (len(self.categorical_columns), n_inputs))
for cc, cc_size in self.categorical_columns.items():
cc_input_var[cc] = tflearn.input_data(shape=[None, 1], name="%s_in" % cc, dtype=tf.int32)
# embedding layers only work on CPU! No GPU implementation in tensorflow, yet!
cc_embed_var[cc] = tflearn.layers.embedding_ops.embedding(cc_input_var[cc], cc_size, 8, name="deep_%s_embed" % cc)
if self.verbose:
print (" %s_embed = %s" % (cc, cc_embed_var[cc]))
flat_vars.append(tf.squeeze(cc_embed_var[cc], squeeze_dims=[1], name="%s_squeeze" % cc))
network = tf.concat(1, [wide_inputs] + flat_vars, name="deep_concat")
for k in range(len(n_nodes)):
network = tflearn.fully_connected(network, n_nodes[k], activation="relu", name="deep_fc%d" % (k+1))
if use_dropout:
network = tflearn.dropout(network, 0.5, name="deep_dropout%d" % (k+1))
if self.verbose:
print ("Deep model network before output %s" % network)
network = tflearn.fully_connected(network, 1, activation="linear", name="deep_fc_output", bias=False)
network = tf.reshape(network, [-1, 1]) # so that accuracy is binary_accuracy
if self.verbose:
print ("Deep model network %s" % network)
return network
Example #14
| Source File: test_layers.py From FRU with MIT License | 4 votes |
|
def test_core_layers(self):
X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]]
Y_nand = [[1.], [1.], [1.], [0.]]
Y_or = [[0.], [1.], [1.], [1.]]
# Graph definition
with tf.Graph().as_default():
# Building a network with 2 optimizers
g = tflearn.input_data(shape=[None, 2])
# Nand operator definition
g_nand = tflearn.fully_connected(g, 32, activation='linear')
g_nand = tflearn.fully_connected(g_nand, 32, activation='linear')
g_nand = tflearn.fully_connected(g_nand, 1, activation='sigmoid')
g_nand = tflearn.regression(g_nand, optimizer='sgd',
learning_rate=2.,
loss='binary_crossentropy')
# Or operator definition
g_or = tflearn.fully_connected(g, 32, activation='linear')
g_or = tflearn.fully_connected(g_or, 32, activation='linear')
g_or = tflearn.fully_connected(g_or, 1, activation='sigmoid')
g_or = tflearn.regression(g_or, optimizer='sgd',
learning_rate=2.,
loss='binary_crossentropy')
# XOR merging Nand and Or operators
g_xor = tflearn.merge([g_nand, g_or], mode='elemwise_mul')
# Training
m = tflearn.DNN(g_xor)
m.fit(X, [Y_nand, Y_or], n_epoch=400, snapshot_epoch=False)
# Testing
self.assertLess(m.predict([[0., 0.]])[0][0], 0.01)
self.assertGreater(m.predict([[0., 1.]])[0][0], 0.9)
self.assertGreater(m.predict([[1., 0.]])[0][0], 0.9)
self.assertLess(m.predict([[1., 1.]])[0][0], 0.01)
# Bulk Tests
with tf.Graph().as_default():
net = tflearn.input_data(shape=[None, 2])
net = tflearn.flatten(net)
net = tflearn.reshape(net, new_shape=[-1])
net = tflearn.activation(net, 'relu')
net = tflearn.dropout(net, 0.5)
net = tflearn.single_unit(net)
Example #15
| Source File: test_models.py From FRU with MIT License | 4 votes |
|
def test_sequencegenerator_words(self):
with tf.Graph().as_default():
text = ["hello","world"]*100
word_idx = {"hello": 0, "world": 1}
maxlen = 2
vec = [x for x in map(word_idx.get, text) if x is not None]
sequences = []
next_words = []
for i in range(0, len(vec) - maxlen, 3):
sequences.append(vec[i: i + maxlen])
next_words.append(vec[i + maxlen])
X = np.zeros((len(sequences), maxlen, len(word_idx)), dtype=np.bool)
Y = np.zeros((len(sequences), len(word_idx)), dtype=np.bool)
for i, seq in enumerate(sequences):
for t, idx in enumerate(seq):
X[i, t, idx] = True
Y[i, next_words[i]] = True
g = tflearn.input_data(shape=[None, maxlen, len(word_idx)])
g = tflearn.lstm(g, 32)
g = tflearn.dropout(g, 0.5)
g = tflearn.fully_connected(g, len(word_idx), activation='softmax')
g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy',
learning_rate=0.1)
m = tflearn.SequenceGenerator(g, dictionary=word_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(4, temperature=.5, seq_seed=["hello","world"])
res_str = " ".join(res[-2:])
self.assertEqual(res_str, "hello world", "SequenceGenerator (word level) test failed! Generated sequence: " + res_str + " expected 'hello world'")
# Testing save method
m.save("test_seqgen_word.tflearn")
self.assertTrue(os.path.exists("test_seqgen_word.tflearn.index"))
# Testing load method
m.load("test_seqgen_word.tflearn")
res = m.generate(4, temperature=.5, seq_seed=["hello","world"])
res_str = " ".join(res[-2:])
self.assertEqual(res_str, "hello world", "Reloaded SequenceGenerator (word level) test failed! Generated sequence: " + res_str + " expected 'hello world'")
