Semi-automated CNN generation procedure

Create Auto_cteate_code,it can create CNN by Semi-automated

some-test/test.py

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+import datetime
+import os
+import numpy as np
+import tensorflow as tf
+from utils_seventyOne_of_Filters import loadData, plot_history_tf, plot_heat_mapProject_PATH = "../some-test\def main():
+    X_train, X_test, y_train, y_test = loadData(RATIO, RANDOM_SEED)
+    if os.path.exists(model_Path_one):
+        print(' get model in h5 file')
+        model = tf.keras.models.load_model(filepath=model_Path_one)
+    else:
+        # create new model(if model unexists)
+        model = CNN_model_level_one()
+        model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
+        model.summary()
+        # TB make
+        tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir,
+                                                              histogram_freq=1,
+                                                              )
+        # Training and validation
+        history = model.fit(X_train, y_train, epochs=NUM_EPOCHS,
+                            batch_size=BATCH_SIZE,
+                            validation_data=(X_test, y_test),
+                            callbacks=[tensorboard_callback])
+        # validation_split=RATIO,
+        model.save(filepath=model_Path_one)
+        plot_history_tf(history)
+    y_pred = np.argmax(model.predict(X_test), axis=-1)
+    plot_heat_map(y_test, y_pred)