target_output,
batch_size=256,
sequence_length=24,
training_len=0.75,
validation_len=0.225)
model = ml.train_model(model,
batch,
validation_data,
x_test_scaled,
y_test_scaled,
callbacks,
epoch_size=25,
epoch_steps=100)
ml.save_model(model, 'model.h5')
output = ml.load_models('model.h5')
import importlib
importlib.reload(ml)
start_idx = 0
length = 500
target_names = target_output
y_pred, y_true, x = ml.plot_comparison(x_test_scaled,
y_test,
y_scaler,
target_names,
output,
length=length)
target_output,
batch_size=256,
sequence_length=24,
training_len=0.9,
validation_len=0.1)
model=ml.train_model(model,
batch,
validation_data,
x_test_scaled,
y_test_scaled,
callbacks,
epoch_size=20,
epoch_steps=100)
ml.save_model(model,'entersolar_model.h5')
output = ml.load_models('entersolar_model.h5')
import importlib
importlib.reload(ml)
start_idx = 0
length = 2000
target_names = target_output
y_pred, y_true,x,mse = ml.plot_comparison(x_test_scaled,y_test,y_scaler,
target_names,output,
start_idx = start_idx,
length=length,
verbose=True)
target_output,
batch_size=256,
sequence_length=10,
training_len=0.85,
validation_len=0.1)
model = ml.train_model(model,
batch,
validation_data,
x_test_scaled,
y_test_scaled,
callbacks,
epoch_size=15,
epoch_steps=100)
ml.save_model(model, 'nsrdb_model.h5')
output = ml.load_models('nsrdb_model.h5')
import importlib
importlib.reload(ml)
start_idx = 0
length = 500
target_names = target_output
y_pred, y_true, x = ml.plot_comparison(x_test_scaled,
y_test,
y_scaler,
target_names,
output,
length=length)
target_output,
batch_size=256,
sequence_length=20,
training_len=0.9,
validation_len=0.1)
model = ml.train_model(model,
batch,
validation_data,
x_test_scaled,
y_test_scaled,
callbacks,
epoch_size=20,
epoch_steps=100)
ml.save_model(model, 'building_demand.h5')
output = ml.load_models('building_demand.h5')
import importlib
importlib.reload(ml)
start_idx = 0
length = 500
target_names = target_output
y_pred, y_true, x, mse = ml.plot_comparison(x_test_scaled,
y_test,
y_scaler,
target_names,
output,
start_idx=start_idx,
length=length,
target_output = ['actual_dc_power', 'ac_power', 'grid_power']
model, batch, validation_data, callbacks, x_test_scaled, y_test_scaled, y_test, y_scaler = ml.model_generation(
df, target_output)
model = ml.train_model(model,
batch,
validation_data,
x_test_scaled,
y_test_scaled,
callbacks,
epoch_size=15,
epoch_steps=100)
ml.save_model(model, 'watson_ibm_model.h5')
output = ml.load_models('watson_ibm_model.h5')
import importlib
importlib.reload(ml)
start_idx = 0
length = 500
target_names = target_output
y_pred, y_true, x = ml.plot_comparison(x_test_scaled,
y_test,
y_scaler,
target_names,
output,
length=length)