# Create the model
x = tf.placeholder(tf.float32, [None, NUM_FEATURES])
y_ = tf.placeholder(tf.float32, [None, NUM_CLASSES])
# Build the graph for the deep net
hidden_layer = layers.dense(x, 10, activation=tf.nn.relu, kernel_initializer=init.orthogonal(
np.sqrt(2)), bias_initializer=init.zeros(), kernel_regularizer=l2_regularizer(1e-6))
output_layer = layers.dense(hidden_layer, 6, kernel_initializer=init.orthogonal(
), bias_initializer=init.zeros(), kernel_regularizer=l2_regularizer(1e-6))
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=y_, logits=output_layer)
loss = tf.reduce_mean(cross_entropy) + get_regularization_loss()
# Create the gradient descent optimizer with the given learning rate.
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.minimize(loss)
correct_prediction = tf.cast(
tf.equal(tf.argmax(output_layer, 1), tf.argmax(y_, 1)), tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_err = []
test_acc = []
for i in range(epochs):
y_ = tf.placeholder(tf.float32, [None, 1])
# Build the graph for the deep net
hidden_layer = layers.dense(x,
30,
activation=tf.nn.relu,
kernel_initializer=init.orthogonal(np.sqrt(2)),
bias_initializer=init.zeros(),
kernel_regularizer=l2_regularizer(1e-3))
output_layer = layers.dense(hidden_layer,
1,
kernel_initializer=init.orthogonal(),
bias_initializer=init.zeros(),
kernel_regularizer=l2_regularizer(1e-3))
loss = tf.reduce_mean(tf.square(y_ - output_layer)) + get_regularization_loss()
#Create the gradient descent optimizer with the given learning rate.
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.minimize(loss)
error = tf.reduce_mean(tf.square(y_ - output_layer))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_err = []
for i in range(epochs):
idm = 0
while idm < 1000:
nidx = idm + 32
train_op.run(feed_dict={x: trainX[idm:nidx], y_: trainY[idm:nidx]})
idm = nidx
tf.reset_default_graph()
with tf.Session() as sess:
# Create the model
lr = tf.placeholder(tf.float32, [])
x = tf.placeholder(tf.float32, [None, NUM_FEATURES])
y_ = tf.placeholder(tf.float32, [None, 1])
# Build the graph for the deep net
hidden_layer = layers.dense(x, neuron_count, activation=tf.nn.relu, kernel_initializer=init.orthogonal(np.sqrt(2)),
bias_initializer=init.zeros(), kernel_regularizer=l2_regularizer(1e-3))
output_layer = layers.dense(hidden_layer, 1, kernel_initializer=init.orthogonal(), bias_initializer=init.zeros(),
kernel_regularizer=l2_regularizer(1e-3))
loss = tf.reduce_mean(tf.square(y_ - output_layer)
) + get_regularization_loss()
# Create the gradient descent optimizer with the given learning rate.
optimizer = tf.train.GradientDescentOptimizer(lr)
train_op = optimizer.minimize(loss)
error = tf.reduce_mean(tf.square(y_ - output_layer))
fold_errors = []
for o in range(NUM_FOLDS):
sess.run(tf.global_variables_initializer())
xTrainX = np.split(trainX, NUM_FOLDS, axis=0)
xTrainY = np.split(trainY, NUM_FOLDS, axis=0)
xValidationX = xTrainX[o]
xValidationY = xTrainY[o]
