[pre-commit.ci] pre-commit autoupdate (#44)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/psf/black: 22.12.0 → 23.3.0](psf/black@22.12.0...23.3.0)
- [github.com/asottile/pyupgrade: v3.3.1 → v3.4.0](asottile/pyupgrade@v3.3.1...v3.4.0)
- [github.com/asottile/setup-cfg-fmt: v2.2.0 → v2.3.0](asottile/setup-cfg-fmt@v2.2.0...v2.3.0)
- [github.com/pycqa/flake8: 5.0.4 → 6.0.0](PyCQA/flake8@5.0.4...6.0.0)

* [pre-commit.ci] auto fixes from pre-commit hooks

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -2,7 +2,7 @@ exclude: .*\.rpt$
 
 repos:
 - repo: https://github.com/psf/black
-  rev: 22.12.0
+  rev: 23.3.0
   hooks:
   - id: black-jupyter
     language_version: python3
@@ -30,18 +30,18 @@ repos:
     args: ["--profile", "black", --line-length=125]
 
 - repo: https://github.com/asottile/pyupgrade
-  rev: v3.3.1
+  rev: v3.4.0
   hooks:
   - id: pyupgrade
     args: ["--py36-plus"]
 
 - repo: https://github.com/asottile/setup-cfg-fmt
-  rev: v2.2.0
+  rev: v2.3.0
   hooks:
   - id: setup-cfg-fmt
 
 - repo: https://github.com/pycqa/flake8
-  rev: 5.0.4
+  rev: 6.0.0
   hooks:
   - id: flake8
     exclude: docs/conf.py

callbacks.py

@@ -70,7 +70,6 @@ class all_callbacks:
     def __init__(
         self, stop_patience=10, lr_factor=0.5, lr_patience=1, lr_epsilon=0.001, lr_cooldown=4, lr_minimum=1e-5, outputDir=''
     ):
-
         self.nl_begin = newline_callbacks_begin(outputDir)
         self.nl_end = newline_callbacks_end()
 

part6_cnns.ipynb

@@ -245,6 +245,7 @@
     "NSTEPS = int(train_size * 0.9) // batch_size  # 90% train, 10% validation in 10-fold cross validation\n",
     "print('Number of training steps per epoch is {}'.format(NSTEPS))\n",
     "\n",
+    "\n",
     "# Prune all convolutional and dense layers gradually from 0 to 50% sparsity every 2 epochs,\n",
     "# ending by the 10th epoch\n",
     "def pruneFunction(layer):\n",
@@ -287,7 +288,6 @@
     "n_epochs = 30\n",
     "\n",
     "if train:\n",
-    "\n",
     "    LOSS = tf.keras.losses.CategoricalCrossentropy()\n",
     "    OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)\n",
     "\n",
@@ -579,7 +579,6 @@
    "outputs": [],
    "source": [
     "def doWeights(model):\n",
-    "\n",
     "    allWeightsByLayer = {}\n",
     "    for layer in model.layers:\n",
     "        if (layer._name).find(\"batch\") != -1 or len(layer.get_weights()) < 1:\n",
@@ -862,7 +861,6 @@
     "\n",
     "\n",
     "def plotROC(Y, y_pred, y_pred_hls4ml, label=\"Model\"):\n",
-    "\n",
     "    accuracy_keras = float(accuracy_score(np.argmax(Y, axis=1), np.argmax(y_pred, axis=1)))\n",
     "    accuracy_hls4ml = float(accuracy_score(np.argmax(Y, axis=1), np.argmax(y_pred_hls4ml, axis=1)))\n",
     "\n",

plotting.py

@@ -54,7 +54,6 @@ def plotRoc(fpr, tpr, auc, labels, linestyle, legend=True):
 
 
 def rocData(y, predict_test, labels):
-
     df = pd.DataFrame()
 
     fpr = {}
@@ -72,7 +71,6 @@ def rocData(y, predict_test, labels):
 
 
 def makeRoc(y, predict_test, labels, linestyle='-', legend=True):
-
     if 'j_index' in labels:
         labels.remove('j_index')