from LinearRegression import my_model
from mnist import model
x = tf.placeholder("float", [None, 784])
x1 = tf.placeholder(tf.float32)
sess = tf.Session()
# restore trained data
with tf.variable_scope("regression"):
y1, variables = model.regression(x)
saver = tf.train.Saver(variables)
saver.restore(sess, "mnist/data/regression.ckpt")
with tf.variable_scope("convolutional"):
keep_prob = tf.placeholder("float")
y2, variables = model.convolutional(x, keep_prob)
saver = tf.train.Saver(variables)
saver.restore(sess, "mnist/data/convolutional.ckpt")
with tf.variable_scope("my_model"):
y3, variables = my_model.regr(x1)
saver = tf.train.Saver(variables)
saver.restore(sess, "LinearRegression/data/linear_regression.ckpt")
decision_tree = joblib.load('DecisionTree/data/parsing_tree.pkl')
knn = joblib.load('KNN/data/knn.pkl')
svm = joblib.load('SVM/data/svm.pkl')
gaussian = joblib.load('Gaussian/data/gaussian.pkl')
def regression(input):
from mnist import model
X1 = tf.placeholder("float", [None, 784])
X2 = tf.placeholder(tf.float32, [None, 28, 28, 1])
sess = tf.Session()
# restore trained data
with tf.variable_scope("regression"):
Y1, variables = model.regression(X1)
saver = tf.train.Saver(variables)
saver.restore(sess, "mnist/data/regression.ckpt")
print("Regression model restored.")
with tf.variable_scope("convolutional"):
pkeep = tf.placeholder(tf.float32)
Y2, Ylogits, variables = model.convolutional(X2, pkeep)
saver = tf.train.Saver(variables)
saver.restore(sess, "mnist/data/convolutional.ckpt")
print("Convolutional model restored.")
def regression(input):
return sess.run(Y1, feed_dict={X1: input}).flatten().tolist()
def convolutional(input):
return sess.run(Y2, feed_dict={X2: input, pkeep: 1.0}).flatten().tolist()
# webapp
app = Flask(__name__)
from mnist import model
x = tf.placeholder("float", [None, 784])
sess = tf.Session()
# restore trained data
with tf.variable_scope("regression"):
y1, variables = model.regression(x)
saver = tf.train.Saver(variables)
saver.restore(sess, "mnist/data/regression.ckpt")
with tf.variable_scope("convolutional"):
keep_prob = tf.placeholder("float")
y2, variables = model.convolutional(x, keep_prob)
saver = tf.train.Saver(variables)
saver.restore(sess, "mnist/data/convolutional.ckpt")
def regression(input):
return sess.run(y1, feed_dict={x: input}).flatten().tolist()
def convolutional(input):
return sess.run(y2, feed_dict={x: input, keep_prob: 1.0}).flatten().tolist()
# webapp
app = Flask(__name__)
db = DataStore()
x = tf.placeholder("float", [None, 784])
sess = tf.Session()
# restore trained data
with tf.variable_scope("perceptron"):
y_percep, perceptron_variables = model.multilayer_perceptron(x)
with tf.variable_scope("regression"):
y_reg, regression_variables = model.regression(x)
with tf.variable_scope("convolutional"):
keep_prob = tf.placeholder(tf.float32)
y_conv, conv_variables = model.convolutional(x, keep_prob)
with tf.variable_scope("rnn"):
y_rnn, _ = model.rnn_network(x)
rnn_variables = tf.get_collection(tf.GraphKeys.VARIABLES, scope='rnn')
saver = tf.train.Saver(conv_variables + perceptron_variables +
regression_variables + rnn_variables)
saver.restore(sess, "mnist/data/mnist.ckpt")
def regression(input):
return sess.run(tf.nn.softmax(y_reg), feed_dict={
x: input
}).flatten().tolist()
x = tf.placeholder("float", [None, 784])
sess = tf.Session()
# restore trained data
with tf.variable_scope("regression"):
logits1, variables1 = model.regression(x)
y1 = tf.nn.softmax(logits1)
saver = tf.train.Saver(variables1)
saver.restore(sess, "mnist/data/regression.ckpt")
with tf.variable_scope("convolutional"):
keep_prob = tf.placeholder("float")
logits2, variables2 = model.convolutional(x, keep_prob)
y2 = tf.nn.softmax(logits2)
saver = tf.train.Saver(variables2)
saver.restore(sess, "mnist/data/convolutional.ckpt")
def regression(input):
return sess.run(y1, feed_dict={x: input}).flatten().tolist()
def convolutional(input):
return sess.run(y2, feed_dict={x: input, keep_prob: 1.0}).flatten().tolist()
# webapp
app = Flask(__name__)
