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Migrate to latest and fix code issues #5

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Migrate to latest and fix code issues #5

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29837e6
use proper string comparison
May 8, 2024
82aa9ac
fix Keras layer imports
May 8, 2024
988399b
migrate to scikeras
May 8, 2024
fe761cd
remove unused "parameter", fixes the tutorial as well
May 8, 2024
dbec6b0
add empty line at EOL
May 8, 2024
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13 changes: 5 additions & 8 deletions Library/Build_Model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,12 +42,10 @@
from keras.layers import Input, Dense, LSTM, Dropout, Flatten, Activation
from keras.layers import Lambda, Reshape, multiply
from keras.layers import concatenate, add, subtract, dot
from keras.wrappers.scikit_learn import KerasRegressor
from keras.layers.core import Activation
from keras.utils.generic_utils import get_custom_objects
from keras.utils.generic_utils import CustomObjectScope
from keras.utils import get_custom_objects, CustomObjectScope
from keras.callbacks import EarlyStopping

from scikeras.wrappers import KerasRegressor
from sklearn import linear_model
from sklearn.model_selection import cross_val_score, KFold
from sklearn.pipeline import Pipeline
Expand Down Expand Up @@ -156,14 +154,13 @@ def Loss_SV(V, S, gradient=False):
dLoss = 0 * V
return Loss_norm, dLoss

def Loss_Vin(V, Pin, Vin, bound, parameter, gradient=False):
def Loss_Vin(V, Pin, Vin, bound, gradient=False):
# Gradient for input boundary constraint
# Loss = ReLU(Pin . V - Vin)
# dLoss = ∂(ReLU(Pin . V - Vin)^2/∂V
# Input: Cf. Gradient_Descent
Pin = tf.convert_to_tensor(np.float32(Pin))
Loss = tf.linalg.matmul(V, tf.transpose(Pin), b_is_sparse=True) - Vin
# tf.cast(tf.multiply(Vin, parameter.scaler), tf.float32)
Loss = tf.keras.activations.relu(Loss) if bound == 'UB' else Loss
Loss_norm = tf.norm(Loss, axis=1, keepdims=True)/Pin.shape[0] # rescaled
if gradient:
Expand Down Expand Up @@ -193,7 +190,7 @@ def Loss_constraint(V, Vin, parameter, gradient=False):
# mean squared sum L2+L3+L4
L2, dL2 = Loss_SV(V, parameter.S, gradient=gradient)
L3, dL3 = Loss_Vin(V, parameter.Pin, Vin,
parameter.mediumbound, parameter, gradient=gradient)
parameter.mediumbound, gradient=gradient)
L4, dL4 = Loss_Vpos(V, parameter, gradient=gradient)
# square sum of L2, L3, L4
L2 = tf.math.square(L2)
Expand All @@ -213,7 +210,7 @@ def Loss_all(V, Vin, Vout, parameter, gradient=False):
L1, dL1 = Loss_Vout(V, parameter.Pout, Vout, gradient=gradient)
L2, dL2 = Loss_SV(V, parameter.S, gradient=gradient)
L3, dL3 = Loss_Vin(V, parameter.Pin, Vin,
parameter.mediumbound, parameter, gradient=gradient)
parameter.mediumbound, gradient=gradient)
L4, dL4 = Loss_Vpos(V, parameter, gradient=gradient)
# square sum of L1, L2, L3, L4
L1 = tf.math.square(L1)
Expand Down
4 changes: 2 additions & 2 deletions Tutorial.ipynb
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Expand Up @@ -611,9 +611,9 @@
"source": [
"# Runs the appropriate method\n",
"\n",
"if model.model_type is 'MM_QP':\n",
"if model.model_type == 'MM_QP':\n",
" Ypred, Stats = MM_QP(model, verbose=True)\n",
"if model.model_type is 'MM_LP':\n",
Comment on lines -614 to -616
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if model.model_type is 'MM_QP':
    Ypred, Stats = MM_QP(model, verbose=True)
if model.model_type is 'MM_LP':
    Ypred, Stats = MM_LP(model, verbose=True)

this warning should be gone after the changes I introduced here. Are you sure you had this branch pulled and were running Tutorial.ipynb?

"if model.model_type == 'MM_LP':\n",
" Ypred, Stats = MM_LP(model, verbose=True)\n",
"\n",
"# Printing results\n",
Expand Down
3 changes: 2 additions & 1 deletion environment_amn.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -16,4 +16,5 @@ dependencies:
- pip
- ipykernel
- pip:
- silence-tensorflow
- silence-tensorflow
- scikeras