def evaluate_rnn_model(single_series, gesture_id, neurons_dim):
tf.reset_default_graph()
# todo put that parameter in configuration class
# cut = CONFIG.max_steps
x = tf.placeholder(tf.float32, [None, None, CONFIG.input_dim])
# seq_length = tf.placeholder(tf.int32, [None])
cell = tf.contrib.rnn.OutputProjectionWrapper(
tf.contrib.rnn.LSTMCell(
num_units=neurons_dim,
activation=CONFIG.get_activation_function(gesture_id)),
output_size=CONFIG.output_dim)
outputs, states = tf.nn.dynamic_rnn(cell, x, dtype=tf.float32)
saver = tf.train.Saver()
with tf.Session() as sess:
# x_new = FilesUtil.cut_and_reshape(single_series, cut)
x_new = FilesUtil.reshape(single_series)
model_path = os.path.join(os.getcwd(), 'model', 'model' + gesture_id,
'rnn_' + gesture_id + '.ckpt')
saver.restore(sess, model_path)
y_pre = sess.run(outputs, feed_dict={x: x_new})
return x_new, y_pre
def create_rnn_model(train_data_sets, validation_data_sets, gesture_id,
batch_size, neurons_dim, n_epochs):
tf.reset_default_graph()
x = tf.placeholder(tf.float32, [None, None, CONFIG.input_dim])
y = tf.placeholder(tf.float32, [None, None, CONFIG.output_dim])
seq_length = tf.placeholder(tf.int32, [None])
cell = tf.contrib.rnn.OutputProjectionWrapper(
tf.contrib.rnn.LSTMCell(
num_units=neurons_dim,
activation=CONFIG.get_activation_function(gesture_id)),
output_size=CONFIG.output_dim)
outputs, states = tf.nn.dynamic_rnn(cell,
x,
dtype=tf.float32,
sequence_length=seq_length)
loss = tf.reduce_mean(tf.square(outputs - y)) # MSE
optimizer = tf.train.AdamOptimizer(learning_rate=CONFIG.learning_rate)
training_op = optimizer.minimize(loss)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
init.run()
sess = loop_epochs(x=x,
y=y,
n_epochs=n_epochs,
training_op=training_op,
loss=loss,
batch_size=batch_size,
train_data_sets=train_data_sets,
validation_data_sets=validation_data_sets,
seq_length=seq_length,
gesture_id=gesture_id,
sess=sess)
# validation_check = 0
# for epoch in range(n_epochs):
# for iteration in range(CONFIG.train_space // batch_size):
# bx, by, s = FilesUtil.feed_next_batch(train_data_sets, batch_size, CONFIG.max_steps)
# vx, vy, s = FilesUtil.feed_next_batch(validation_data_sets, batch_size, CONFIG.max_steps)
# sess.run(training_op, feed_dict={x: np.asarray(bx), y: np.asarray(by), seq_length: np.asarray(s)})
# mse_train = loss.eval(feed_dict={x: np.asarray(bx), y: np.asarray(by), seq_length: np.asarray(s)})
# mse_val = loss.eval(feed_dict={x: np.asarray(vx), y: np.asarray(vy), seq_length: np.asarray(s)})
# perc_diff = FilesUtil.get_percent_diff(mse_train, mse_val)
# print("EPOCH=", epoch, "MSE_train", mse_train, "MSE_validate", mse_val, "Percentage Diff",
# perc_diff, "Gesture ", gesture_id)
# if mse_train < 1.0 and mse_val < 1.0 and perc_diff > 10.0:
# validation_check += 1
# elif validation_check == 3:
# print("exiting....")
# break
# if validation_check == 3:
# print("exiting..")
# break
# # print("EPOCH=", epoch, "MSE_validate", mse)
# if epoch % 100 == 0:
# print("epoch", epoch, "completed")
model_path = os.path.join(os.getcwd(), 'model', 'model' + gesture_id,
'rnn_' + gesture_id + '.ckpt')
saver.save(sess, model_path)