def test_dnn(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"))
with tf.Graph().as_default():
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 load method
m.load("test_dnn.tflearn")
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")
def test_dnn(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"))
with tf.Graph().as_default():
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 load method
m.load("test_dnn.tflearn")
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")
def __init__(self, s_date, n_frame):
self.n_epoch = 20
prev_bd = int(s_date[:6])-1
prev_ed = int(s_date[9:15])-1
if prev_bd%100 == 0: prev_bd -= 98
if prev_ed%100 == 0: prev_ed -= 98
pred_s_date = "%d01_%d01" % (prev_bd, prev_ed)
prev_model = '../model/tflearn/reg_l3_bn/big/%s' % pred_s_date
self.model_dir = '../model/tflearn/reg_l3_bn/big/%s' % s_date
tf.reset_default_graph()
tflearn.init_graph(gpu_memory_fraction=0.1)
input_layer = tflearn.input_data(shape=[None, 23*n_frame], name='input')
dense1 = tflearn.fully_connected(input_layer, 400, name='dense1', activation='relu')
dense1n = tflearn.batch_normalization(dense1, name='BN1')
dense2 = tflearn.fully_connected(dense1n, 100, name='dense2', activation='relu')
dense2n = tflearn.batch_normalization(dense2, name='BN2')
dense3 = tflearn.fully_connected(dense2n, 1, name='dense3')
output = tflearn.single_unit(dense3)
regression = tflearn.regression(output, optimizer='adam', loss='mean_square',
metric='R2', learning_rate=0.001)
self.estimators = tflearn.DNN(regression)
if os.path.exists('%s/model.tfl' % prev_model):
self.estimators.load('%s/model.tfl' % prev_model)
self.n_epoch = 10
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
def linear_regression():
# 回归数据
X = [
3.38, 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
]
# 线性回归图
input_ = tflearn.input_data(shape=[None])
linear = tflearn.single_unit(input_)
# 优化器，目标（`optimizer`）和评价指标（`metric`）
regression = tflearn.regression(linear,
optimizer='sgd',
loss='mean_square',
metric='R2',
learning_rate=0.01)
# 使用`DNN`（深度神经网络）模型类训练模型
m = tflearn.DNN(regression)
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
print("回归结果：")
# `get_weights`方法获取模型的权重值
print("Y = " + str(m.get_weights(linear.W)) + "*X + " +
str(m.get_weights(linear.b)))
print("x的测试预测 = 3.2, 3.3, 3.4:")
print(m.predict([3.2, 3.3, 3.4]))
def __init__(self):
if not available:
raise ImportError("You don't have tensorflow")
input_ = tflearn.input_data(shape=[None])
linear = tflearn.single_unit(input_)
regression = tflearn.regression(linear)
self.m = tflearn.DNN(regression)
def __init__(self):
self.len_past = 30
#self.s_date = "20120101_20160330"
#self.model_dir = '../model/tflearn/reg_l3_bn/big/%s/' % self.s_date
tf.reset_default_graph()
tflearn.init_graph(gpu_memory_fraction=0.05)
input_layer = tflearn.input_data(shape=[None, 690], name='input')
dense1 = tflearn.fully_connected(input_layer,
400,
name='dense1',
activation='relu')
dense1n = tflearn.batch_normalization(dense1, name='BN1')
dense2 = tflearn.fully_connected(dense1n,
100,
name='dense2',
activation='relu')
dense2n = tflearn.batch_normalization(dense2, name='BN2')
dense3 = tflearn.fully_connected(dense2n, 1, name='dense3')
output = tflearn.single_unit(dense3)
regression = tflearn.regression(output,
optimizer='adam',
loss='mean_square',
metric='R2',
learning_rate=0.001)
self.estimators = tflearn.DNN(regression)
self.qty = {}
self.day_last = {}
self.currency = 100000000
def get_network_spec():
network = tflearn.input_data(shape=[None])
network = tflearn.single_unit(network)
network = tflearn.regression(network,
optimizer='sgd',
loss='mean_square',
metric='R2',
learning_rate=0.01)
return network
def case_linear_regression():
x = [1,2,3,4,5]
y = [2,4,6,8,10]
net = tflearn.input_data([None])
linear = tflearn.single_unit(net)
net = tflearn.regression(linear, optimizer='sgd', loss='mean_square',
metric='R2', learning_rate=0.01)
model = tflearn.DNN(net)
model.fit(x, y, n_epoch=200, snapshot_epoch=False,show_metric=True,batch_size=1)
print model.predict([8,9]) # [15.990408897399902, 17.988374710083008]
print model.get_weights(linear.W) # [ 1.99796414]
print model.get_weights(linear.b) # [ 0.00669619]
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"))
# 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")
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)
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)
def __init__(self, s_date):
prev_bd = int(s_date[:6])-1
prev_ed = int(s_date[9:15])-1
if prev_bd%100 == 0: prev_bd -= 98
if prev_ed%100 == 0: prev_ed -= 98
pred_s_date = "%d01_%d01" % (prev_bd, prev_ed)
prev_model = '../model/tflearn/lstm/%s' % pred_s_date
self.model_dir = '../model/tflearn/lstm/%s' % s_date
tf.reset_default_graph()
tflearn.init_graph(gpu_memory_fraction=0.1)
input_layer = tflearn.input_data(shape=[None, 30, 23], name='input')
lstm1 = tflearn.lstm(input_layer, 23, dynamic=True, name='lstm1')
dense1 = tflearn.fully_connected(lstm1, 1, name='dense1')
output = tflearn.single_unit(dense1)
regression = tflearn.regression(output, optimizer='adam', loss='mean_square',
metric='R2', learning_rate=0.001)
self.estimators = tflearn.DNN(regression)
if os.path.exists('%s/model.tfl' % prev_model):
self.estimators.load('%s/model.tfl' % prev_model)
def __init__(self):
self.len_past = 30
#self.s_date = "20120101_20160330"
#self.model_dir = '../model/tflearn/reg_l3_bn/big/%s/' % self.s_date
tf.reset_default_graph()
tflearn.init_graph(gpu_memory_fraction=0.05)
input_layer = tflearn.input_data(shape=[None, 690], name='input')
dense1 = tflearn.fully_connected(input_layer, 400, name='dense1', activation='relu')
dense1n = tflearn.batch_normalization(dense1, name='BN1')
dense2 = tflearn.fully_connected(dense1n, 100, name='dense2', activation='relu')
dense2n = tflearn.batch_normalization(dense2, name='BN2')
dense3 = tflearn.fully_connected(dense2n, 1, name='dense3')
output = tflearn.single_unit(dense3)
regression = tflearn.regression(output, optimizer='adam', loss='mean_square',
metric='R2', learning_rate=0.001)
self.estimators = tflearn.DNN(regression)
self.qty = {}
self.day_last = {}
self.currency = 100000000
def __init__(self, s_date):
prev_bd = int(s_date[:6]) - 1
prev_ed = int(s_date[9:15]) - 1
if prev_bd % 100 == 0: prev_bd -= 98
if prev_ed % 100 == 0: prev_ed -= 98
pred_s_date = "%d01_%d01" % (prev_bd, prev_ed)
prev_model = '../model/tflearn/lstm/%s' % pred_s_date
self.model_dir = '../model/tflearn/lstm/%s' % s_date
tf.reset_default_graph()
tflearn.init_graph(gpu_memory_fraction=0.1)
input_layer = tflearn.input_data(shape=[None, 30, 23], name='input')
lstm1 = tflearn.lstm(input_layer, 23, dynamic=True, name='lstm1')
dense1 = tflearn.fully_connected(lstm1, 1, name='dense1')
output = tflearn.single_unit(dense1)
regression = tflearn.regression(output,
optimizer='adam',
loss='mean_square',
metric='R2',
learning_rate=0.001)
self.estimators = tflearn.DNN(regression)
if os.path.exists('%s/model.tfl' % prev_model):
self.estimators.load('%s/model.tfl' % prev_model)
def __init__(self, s_date, n_frame):
self.n_epoch = 20
prev_bd = int(s_date[:6]) - 1
prev_ed = int(s_date[9:15]) - 1
if prev_bd % 100 == 0: prev_bd -= 98
if prev_ed % 100 == 0: prev_ed -= 98
pred_s_date = "%d01_%d01" % (prev_bd, prev_ed)
prev_model = '../model/tflearn/reg_l3_bn/big/%s' % pred_s_date
self.model_dir = '../model/tflearn/reg_l3_bn/big/%s' % s_date
tf.reset_default_graph()
tflearn.init_graph(gpu_memory_fraction=0.1)
input_layer = tflearn.input_data(shape=[None, 23 * n_frame],
name='input')
dense1 = tflearn.fully_connected(input_layer,
400,
name='dense1',
activation='relu')
dense1n = tflearn.batch_normalization(dense1, name='BN1')
dense2 = tflearn.fully_connected(dense1n,
100,
name='dense2',
activation='relu')
dense2n = tflearn.batch_normalization(dense2, name='BN2')
dense3 = tflearn.fully_connected(dense2n, 1, name='dense3')
output = tflearn.single_unit(dense3)
regression = tflearn.regression(output,
optimizer='adam',
loss='mean_square',
metric='R2',
learning_rate=0.001)
self.estimators = tflearn.DNN(regression)
if os.path.exists('%s/model.tfl' % prev_model):
self.estimators.load('%s/model.tfl' % prev_model)
self.n_epoch = 10
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
import tflearn
# Regression data
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)
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
print("\n Regression result: ")
print('Y = {} * X + {}'.format(m.get_weights(linear.W), m.get_weights(linear.b)))
print("\nTest prediction for x = 3.2, 3.3, 3.4:")
print(m.predict([3.2, 3.3, 3.4]))
""" Linear Regression Example """
from __future__ import absolute_import, division, print_function
import tflearn
# Regression data
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]
# Linear Regression graph
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)
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
print("\nRegression result:")
print("Y = " + str(m.get_weights(linear.W)) +
"*X + " + str(m.get_weights(linear.b)))
print("\nTest prediction for x = 3.2, 3.3, 3.4:")
print(m.predict([3.2, 3.3, 3.4]))
# should output (close, not exact) y = [1.5315033197402954, 1.5585315227508545, 1.5855598449707031]
#!/usr/bin/python
""" Linear Regression Example """
from __future__ import absolute_import, division, print_function
import tflearn
# Regression data
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]
# Linear Regression graph
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)
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
print("\nRegression result:")
print("Y = " + str(m.get_weights(linear.W)) +
"*X + " + str(m.get_weights(linear.b)))
print("\nTest prediction for x = 3.2, 3.3, 3.4:")
print(m.predict([3.2, 3.3, 3.4]))
# should output (close, not exact) y = [1.5315033197402954, 1.5585315227508545, 1.5855598449707031
import tflearn from tflearn.models.dnn import DNN from tflearn.models.generator import SequenceGenerator from tflearn.data_utils import VocabularyProcessor from tflearn.data_preprocessing import DataPreprocessing from tflearn.helpers.trainer import Trainer from tflearn.helpers.evaluator import Evaluator from tflearn.helpers.summarizer import summarize from tflearn.helpers.regularizer import add_weights_regularizer from tensorflow.contrib.slim import dataset from tensorflow.contrib.slim import dataset tflearn.input_data() tflearn.variable() tflearn.conv_2d() tflearn.single_unit() tflearn.lstm() tflearn.embedding() tflearn.batch_normalization() tflearn.merge() tflearn.regression() tflearn.tanh() tflearn.softmax_categorical_crossentropy() tflearn.SGD() tflearn.initializations.uniform() tflearn.losses.L1() tflearn.add_weights_regularizer() tflearn.metrics.Accuracy() tflearn.summaries() tflearn.ImagePreprocessing() tflearn.ImageAugmentation()
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")
tratio = 0.1 #ratio of testing to training
inputDayLength = 5
# Neural Network Model Setup (regression)
inlayer = tflearn.layers.core.input_data(shape=[inputDayLength, len(tfields)])
layer1 = tflearn.layers.core.fully_connected(
inlayer, ((inputDayLength % 2) + 1) * len(tfields), activation='linear')
layer2 = tflearn.layers.core.fully_connected(
layer1, ((inputDayLength % 4) + 1) * len(tfields), activation='linear')
layer3 = tflearn.layers.core.fully_connected(
layer2, ((inputDayLength % 16) + 1) * len(tfields), activation='linear')
#layer4 = tflearn.layers.core.fully_connected(layer2,10, activation = 'linear')
layer5 = tflearn.layers.core.fully_connected(
layer3, 1, activation='linear'
) #final layer needs to be only one neuron wide for regression to work
linear = tflearn.single_unit(layer5)
nOptimizer = tflearn.optimizers.Momentum(learning_rate=0.003, decay_step=1000)
regression = tflearn.layers.estimator.regression(linear,
optimizer=nOptimizer,
loss='mean_square')
m = tflearn.DNN(regression)
'''
# Neural Network Model Setup (categorical)
net = tflearn.layers.core.input_data(shape=[90,len(tfields)])
net = tflearn.layers.core.fully_connected(net,45*len(tfields), activation = 'relu')
net = tflearn.layers.core.fully_connected(net,5*len(tfields), activation = 'relu')
#net = tflearn.layers.core.fully_connected(net,500, activation = 'relu')
#net = tflearn.layers.core.fully_connected(net,50, activation = 'relu')
net = tflearn.layers.core.dropout(net,0.8)
net = tflearn.layers.core.fully_connected(net,5, activation = 'softmax')
#net = tflearn.single_unit(net)
import tflearn
import tensorflow as tf
import numpy as np
#training data
x_train = [1, 2, 3, 4, 5]
y_train = [2, 4, 6, 8, 10]
#testing data
x_test = [6, 7, 8, 9, 10]
y_test = [12, 14, 16, 18, 20]
tf.reset_default_graph()
# build neural network
net = tflearn.input_data(shape=[None])
net = tflearn.single_unit(net)
# net = tflearn.fully_connected(net,1, activation='softmax')
net = tflearn.regression(net,
loss='mean_square',
optimizer='adam',
learning_rate=0.01)
model = tflearn.DNN(net, tensorboard_dir='tflearn_logs')
model.fit(x_train, y_train, n_epoch=1000, show_metric=True)
classes = model.predict(x_test)
print(classes)
