Add environment.yml and some minor fixes

environment.yml

@@ -0,0 +1,17 @@
+name: enet-script-env
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.9.5
+  - pip=23.3.2
+  - pip:
+      - git+https://github.com/nicologhielmetti/hls4ml.git@enet#egg=hls4ml[profiling]
+      - torch==2.1.2
+      - keras==2.7.0
+      - numpy==1.26.1
+      - qkeras==0.9.0
+      - seaborn==0.13.1
+      - tensorflow==2.7.1
+      - tensorflow-datasets==4.9.3
+      - opencv-python==4.9.0.80

generate_y_hls.py

@@ -1,8 +1,8 @@
 import hls4ml
 import numpy as np
-hls4ml_model_path = '/eos/home-n/nghielme/enet-results-run2/results_hls_f8_clk7_rf6_q4_ap_fixed_8-4_/hls_f8_clk7_rf6_q4_ap_fixed_8-4__FIFO_OPT'
+hls4ml_model_path = './hls4ml_example_output'
 hls_model = hls4ml.converters.convert_from_config(hls4ml_model_path + '/hls4ml_config.yml')
 hls_model.compile()
-X = np.load('X_test.npy')
+X = np.load('./data_pickles/X_test.npy')
 y_hls = hls_model.predict(X)
-np.save('y_hls.npy', y_hls)
+np.save('./data_pickles/y_hls.npy', y_hls)

generate_y_keras.py

@@ -2,11 +2,10 @@
 import os
 import numpy as np
 
-X = np.load('X_test.npy')
+X = np.load('./data_pickles/X_test.npy')
 for model in os.listdir('models_h5_run2'):
     if model.endswith('.h5'):
         keras_model = load_qmodel('models_h5_run2/' + model, compile=False)
         y_keras = keras_model(X)
-        np.save('models_h5_run2/y_keras_' + model[:8] + '.npy')
-    else:
-        continue
+        np.save('models_h5_run2/y_keras_' + model[:8] + '.npy', y_keras)
+