Mean Square Error not calculated correctly
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1
I am using the RNN for time series prediction. Here are details of my model:
Loss Function: Mean Square Error
Optimizer: Adam Optimizer
The Input data is scaled between 0 and 1, and also input data doesn't contain any "nan" values.
The issue is while execution of the model. After 100-200 epocs, the MSE would show the value as "nan".
Any sights on what could have caused the issue?
Here is the code for my model.
n_steps_Begin=n_training_samples
n_features_Begin=train_store_Begin.shape[2] # Number of features to be used. To begin with, using only 'Sales' as Input Feature
n_neurons_Begin=50 # Number of neurons on each Cell
n_outputs_Begin=1 # 1 outout, since only Sales has to be predicted.
learning_rate_Begin=0.001
n_iterations_Begin=10000
tf.reset_default_graph()
with tf.name_scope("TrainingData"):
X_Begin=tf.placeholder(tf.float32, [None, n_steps_Begin, n_features_Begin], name="InputData")
y_Begin=tf.placeholder(tf.float32, [None, n_steps_Begin, n_outputs_Begin], name="OutputData")
with tf.name_scope("RecurrentNeuralNetwork"):
cell_Begin=tf.contrib.rnn.OutputProjectionWrapper(
tf.contrib.rnn.LSTMCell(num_units=n_neurons_Begin, activation=tf.nn.elu),
output_size=n_outputs_Begin)
outputs_Begin,states_Begin=tf.nn.dynamic_rnn(cell_Begin, X_Begin, dtype=tf.float32)
with tf.name_scope("LossFunction"):
loss_Begin=tf.reduce_mean(tf.square(outputs_Begin-y_Begin))
optimizer_Begin=tf.train.AdamOptimizer(learning_rate=learning_rate_Begin)
training_op_Begin=optimizer_Begin.minimize(loss_Begin)
init=tf.global_variables_initializer()
python deep-learning recurrent-neural-network
asked Jan 4 at 10:30
RajatRajat
252
add a comment |
1
I am using the RNN for time series prediction. Here are details of my model:
Loss Function: Mean Square Error
Optimizer: Adam Optimizer
The Input data is scaled between 0 and 1, and also input data doesn't contain any "nan" values.
The issue is while execution of the model. After 100-200 epocs, the MSE would show the value as "nan".
Any sights on what could have caused the issue?
Here is the code for my model.
n_steps_Begin=n_training_samples
n_features_Begin=train_store_Begin.shape[2] # Number of features to be used. To begin with, using only 'Sales' as Input Feature
n_neurons_Begin=50 # Number of neurons on each Cell
n_outputs_Begin=1 # 1 outout, since only Sales has to be predicted.
learning_rate_Begin=0.001
n_iterations_Begin=10000
tf.reset_default_graph()
with tf.name_scope("TrainingData"):
X_Begin=tf.placeholder(tf.float32, [None, n_steps_Begin, n_features_Begin], name="InputData")
y_Begin=tf.placeholder(tf.float32, [None, n_steps_Begin, n_outputs_Begin], name="OutputData")
with tf.name_scope("RecurrentNeuralNetwork"):
cell_Begin=tf.contrib.rnn.OutputProjectionWrapper(
tf.contrib.rnn.LSTMCell(num_units=n_neurons_Begin, activation=tf.nn.elu),
output_size=n_outputs_Begin)
outputs_Begin,states_Begin=tf.nn.dynamic_rnn(cell_Begin, X_Begin, dtype=tf.float32)
with tf.name_scope("LossFunction"):
loss_Begin=tf.reduce_mean(tf.square(outputs_Begin-y_Begin))
optimizer_Begin=tf.train.AdamOptimizer(learning_rate=learning_rate_Begin)
training_op_Begin=optimizer_Begin.minimize(loss_Begin)
init=tf.global_variables_initializer()
python deep-learning recurrent-neural-network
asked Jan 4 at 10:30
RajatRajat
252
add a comment |
1
1
1
I am using the RNN for time series prediction. Here are details of my model:
Loss Function: Mean Square Error
Optimizer: Adam Optimizer
The Input data is scaled between 0 and 1, and also input data doesn't contain any "nan" values.
The issue is while execution of the model. After 100-200 epocs, the MSE would show the value as "nan".
Any sights on what could have caused the issue?
Here is the code for my model.
n_steps_Begin=n_training_samples
n_features_Begin=train_store_Begin.shape[2] # Number of features to be used. To begin with, using only 'Sales' as Input Feature
n_neurons_Begin=50 # Number of neurons on each Cell
n_outputs_Begin=1 # 1 outout, since only Sales has to be predicted.
learning_rate_Begin=0.001
n_iterations_Begin=10000
tf.reset_default_graph()
with tf.name_scope("TrainingData"):
X_Begin=tf.placeholder(tf.float32, [None, n_steps_Begin, n_features_Begin], name="InputData")
y_Begin=tf.placeholder(tf.float32, [None, n_steps_Begin, n_outputs_Begin], name="OutputData")
with tf.name_scope("RecurrentNeuralNetwork"):
cell_Begin=tf.contrib.rnn.OutputProjectionWrapper(
tf.contrib.rnn.LSTMCell(num_units=n_neurons_Begin, activation=tf.nn.elu),
output_size=n_outputs_Begin)
outputs_Begin,states_Begin=tf.nn.dynamic_rnn(cell_Begin, X_Begin, dtype=tf.float32)
with tf.name_scope("LossFunction"):
loss_Begin=tf.reduce_mean(tf.square(outputs_Begin-y_Begin))
optimizer_Begin=tf.train.AdamOptimizer(learning_rate=learning_rate_Begin)
training_op_Begin=optimizer_Begin.minimize(loss_Begin)
init=tf.global_variables_initializer()
python deep-learning recurrent-neural-network
asked Jan 4 at 10:30
RajatRajat
252
I am using the RNN for time series prediction. Here are details of my model:
Loss Function: Mean Square Error
Optimizer: Adam Optimizer
The Input data is scaled between 0 and 1, and also input data doesn't contain any "nan" values.
The issue is while execution of the model. After 100-200 epocs, the MSE would show the value as "nan".
Any sights on what could have caused the issue?
Here is the code for my model.
n_steps_Begin=n_training_samples
n_features_Begin=train_store_Begin.shape[2] # Number of features to be used. To begin with, using only 'Sales' as Input Feature
n_neurons_Begin=50 # Number of neurons on each Cell
n_outputs_Begin=1 # 1 outout, since only Sales has to be predicted.
learning_rate_Begin=0.001
n_iterations_Begin=10000
tf.reset_default_graph()
with tf.name_scope("TrainingData"):
X_Begin=tf.placeholder(tf.float32, [None, n_steps_Begin, n_features_Begin], name="InputData")
y_Begin=tf.placeholder(tf.float32, [None, n_steps_Begin, n_outputs_Begin], name="OutputData")
with tf.name_scope("RecurrentNeuralNetwork"):
cell_Begin=tf.contrib.rnn.OutputProjectionWrapper(
tf.contrib.rnn.LSTMCell(num_units=n_neurons_Begin, activation=tf.nn.elu),
output_size=n_outputs_Begin)
outputs_Begin,states_Begin=tf.nn.dynamic_rnn(cell_Begin, X_Begin, dtype=tf.float32)
with tf.name_scope("LossFunction"):
loss_Begin=tf.reduce_mean(tf.square(outputs_Begin-y_Begin))
optimizer_Begin=tf.train.AdamOptimizer(learning_rate=learning_rate_Begin)
training_op_Begin=optimizer_Begin.minimize(loss_Begin)
init=tf.global_variables_initializer()
python deep-learning recurrent-neural-network
python deep-learning recurrent-neural-network
asked Jan 4 at 10:30
RajatRajat
252
asked Jan 4 at 10:30
RajatRajat
252
asked Jan 4 at 10:30
RajatRajat
252
asked Jan 4 at 10:30
RajatRajat
252
asked Jan 4 at 10:30
RajatRajat
252
252
add a comment |
add a comment |
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