keras2matlab.py

model_training/ce-clm_training/cen_training/keras2matlab.py

@@ -0,0 +1,71 @@
+import os
+import sys
+import time
+import pickle
+import scipy.io as sio
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation
+from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax
+from keras.constraints import maxnorm, nonneg
+import numpy
+import scipy
+from scipy import io
+from itertools import product
+import pickle
+import time
+import uuid
+import os
+import h5py
+import sys
+from keras.models import model_from_json
+from keras.models import model_from_yaml
+
+
+
+
+#accuracies is a list of (rmse_i and correlation_i) for i in range of your epochs - we want to pick the model from somwhere in your  
+
+def dump(keras_weight_file,output_filename,rmse,corr):
+
+        #build the model
+        model=build_model()
+        model.load_weights(keras_weight_file)
+        denses=[layer for layer in model.layers if type(layer) is keras.layers.Dense]
+        #creating the matlab object
+        final=numpy.zeros(len(denses)*2,dtype=numpy.object)
+        #getting all the weights and biases in their correspoding place in final object
+        for i in range(len(denses)):
+                w,b=(denses[i].get_weights())
+                final[i*2]=w
+                final[i*2+1]=b
+        #writing weights,rmse,correlation
+        sio.savemat(output_filename, {'rmse':rmse,'correlation_2':corr,'weights':final})
+
+
+#this is the arch4 - #TODO BY YOU: replace with your architecture - just make sure the loop inside keras2matlab.py is working correctly with your architecture
+
+def build_model():
+
+        model = Sequential()
+        model.add(Dense(500, input_dim=122, activation='relu'))
+        model.add(Dropout(0.3))
+        model.add(Dense(200, activation='relu'))
+        model.add(Dropout(0.3))
+        model.add(Dense(100, activation='sigmoid'))
+        model.add(Dropout(0.3))
+
+        model.add(Dense(1, W_constraint=nonneg(), activation='sigmoid'))
+
+
+        optimizers=[];
+        optimizers.append(SGD(lr=.1, momentum=0.1, decay=0.0))
+        optimizers.append(RMSprop(lr=0.001,rho=0.9, epsilon=1e-06))
+        optimizers.append(Adagrad(lr=0.01, epsilon=1e-06))
+        optimizers.append(Adadelta(lr=1.0, rho=0.95, epsilon=1e-06))
+        optimizers.append(Adam(lr=0.0001/2, beta_1=0.9, beta_2=0.999, epsilon=1e-08))
+        optimizers.append(Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08))
+
+
+        model.compile(loss='mean_squared_error', optimizer=optimizers[4])
+        return model