Model

[sjdhola]

Here when we train the model we are getting really good accuracy about 87.30% but when we save this model and evaluate the image dataset using that saved model the accuracy is decreasing to 21%.

[201801196]

I've trained the model again. now we are getting 88.50% validation accuracy on evaluation using saved model.

[khyatibhuva]

Noted. Can you upload your files in this issue as well?

[201801196]

import tensorflow as tf from tensorflow.keras import from tensorflow.keras.layers import from tensorflow.keras.models import Model from tensorflow.keras.applications.vgg16 import VGG16 from tensorflow.keras.applications.vgg16 import preprocess_input from tensorflow.keras.preprocessing import image from tensorflow.keras.models import load_model from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.models import Sequential from keras.applications.mobilenet import MobileNet, preprocess_input import numpy as np from glob import glob import matplotlib.pyplot as plt

IMAGE_SIZE = [227, 227]

model =MobileNet(input_shape= [227,227, 3], weights='imagenet', include_top=False)

for layer in model.layers[:-23]: layer.trainable = False

folders = glob('/content/drive/MyDrive/Colab Notebooks/Project/Cars96/training/*')

x = Flatten()(model.layers[-6].output) prediction = Dense(len(folders), activation='softmax')(x) model = Model(inputs = model.input, outputs = prediction)

model.summary()

adam=tf.keras.optimizers.Adam( learning_rate=1e-5, beta_1=0.9, beta_2=0.999, epsilon=1e-07, decay = 0.0 ) model.compile( loss='categorical_crossentropy', optimizer="adam", metrics=['accuracy'] )

from tensorflow.keras.preprocessing.image import ImageDataGenerator train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input,

rescale = 1./255,

                               rotation_range = 0,
                               zoom_range = 0.2,
                               horizontal_flip = True)

test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input) training_set = train_datagen.flow_from_directory('/content/drive/MyDrive/Colab Notebooks/Project/Cars96/training', target_size = (227, 227), batch_size = 256, class_mode = 'categorical', shuffle=False) test_set = test_datagen.flow_from_directory('/content/drive/MyDrive/Colab Notebooks/Project/Cars96/testing', target_size = (227, 227), batch_size = 256, class_mode = 'categorical', shuffle=False)

checkpoint_filepath = '/content/drive/MyDrive/Colab Notebooks/Project/sample/./weights.{epoch:02d}.hdf5' callback = [tf.keras.callbacks.EarlyStopping(monitor='loss', patience=4,mode = 'auto', restore_best_weights=True), tf.keras.callbacks.CSVLogger('traininglog.csv', separator=",", append=False), tf.keras.callbacks.ReduceLROnPlateau(monitor="val_loss",factor=0.1, patience=2, mode="auto",min_delta=0.0001,min_lr=0), tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filepath, save_weights_only=True,monitor='val_accuracy',mode='max',save_best_only=True), ] r = model.fit( training_set, validation_data=test_set, epochs=15, steps_per_epoch=len(training_set), validation_steps=len(test_set), callbacks = [callback] )

Visualizing the results.

plt.plot(r.history['loss'], label='train loss') plt.plot(r.history['val_loss'], label='val loss') plt.legend() plt.show() plt.plot(r.history['accuracy'], label='train acc') plt.plot(r.history['val_accuracy'], label='val acc') plt.legend() plt.show()

model.save('New_model.h5') model.save('/content/drive/MyDrive/Colab Notebooks/Project/Cars96/New_model_100_classes_2.h5') model.evaluate(test_set)

[khyatibhuva]

Noted.