Updated all models October 2024

Models/model_lenet5.py

@@ -1,45 +1,25 @@
 import sys
 import os
 
-from keras.layers.pooling import MaxPool2D 
-
-sys.path.append('c:/git/keras')
-sys.path.append('c:/git/absl')
-
-# installed 
-# py -mpip install numpy
-# py -mpip show numpy 
-# py -mpip install tensorflow
-# py -mpip show tensorflow
-# py -mpip install matplotlib
-# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
-
-import tensorflow
+from keras.optimizers import SGD
+from keras.optimizers import Adam
+from tensorflow.keras.callbacks import TensorBoard
 import keras
-from keras.optimizers import adam_v2
-from tensorflow.keras.optimizers import Adam
-from keras.datasets import cifar10
-from keras.preprocessing.image import ImageDataGenerator
-from keras.models import Sequential
-from keras.utils import np_utils
-from keras.layers import Dense
-from keras.layers import Activation
-from keras.layers import Flatten
-from keras.layers import Dropout
-from keras.layers import BatchNormalization
-from keras.layers import Conv2D
-from keras.layers import MaxPooling2D
-from keras.layers import AveragePooling2D
-from keras.callbacks import EarlyStopping, ModelCheckpoint
-from keras import regularizers
-from keras import optimizers
-from matplotlib import pyplot
+from keras.src.legacy.preprocessing.image import ImageDataGenerator
 from keras.callbacks import ModelCheckpoint
-from keras.callbacks import LearningRateScheduler
-from keras.preprocessing.image import load_img
-from keras.preprocessing.image import img_to_array
-from keras.preprocessing.image import array_to_img
-from keras.optimizer_v2 import gradient_descent 
+from tensorflow.keras.callbacks import EarlyStopping
+from keras.layers import Flatten
+from keras.layers import Dense
+from keras.layers import MaxPool2D
+from keras.layers import Dropout
+from keras.models import Model
+from keras.layers import AveragePooling2D
+from keras.models import Sequential
+import numpy as np
+import tensorflow
+from inception_module import *
+import math
+from time import time
 
 #Load some data
 #(x_train,y_train), (x_test,y_test)=cifar10.load_data()
@@ -50,7 +30,7 @@ batch_size = 256
 learning_rate=.00001
 epochs=1000
 patience_on_early_stop=10
-model_name='lenet5.h5'
+model_name='lenet5.h5.keras'
 
 train_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
     'C:\\boneyard\\DeepLearning\\data',
@@ -73,6 +53,9 @@ val_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
     seed=1337
 )
 
+# Create a TensorBoard instance with the path to the logs directory.  Before training, in Terminal, run tensorboard --logdir=logs/
+tensorboard = TensorBoard(log_dir='logs/{}'.format(time()))
+
 model=Sequential()
 
 #C1
@@ -100,7 +83,7 @@ model.add(Dense(units=1,activation='sigmoid'))
 
 model.summary()
 
-optimizer=gradient_descent.SGD(learning_rate=learning_rate, momentum=0.9, nesterov=False)
+optimizer=SGD(learning_rate=learning_rate, momentum=0.9, nesterov=False)
 
 checkpointer=ModelCheckpoint(filepath=model_name,monitor='accuracy',verbose=1,save_best_only=True)
 
@@ -108,7 +91,7 @@ early_stopping=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=pa
 
 model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy'])
 
-history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,callbacks=[checkpointer,early_stopping])
+history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,callbacks=[tensorboard,checkpointer,early_stopping])
 
 # plot the learning curves
 pyplot.plot(history.history['accuracy'],label='train')