In the beginning, I made a CNN model train to predict cats vs dogs.
it was unstable during training however I get 2000 photos for training and another 1000 validation data set so that thought the problem with my model and I load a pre-trained model resnet50 and made a fully connected layer with a softmax of 2 outputs.
and it is still unstable.
I changed the batch_size, epochs, and steps_per_epochs much time and it is still unstable.
what should I do and what is my fault
#Data base
from os.path import join as p
from os import getcwd as g
train = p(g(), 'train')
validation = p(g(), 'validation')
train_cat = p(train, 'cat')
train_dog = p(train, 'dog')
validation_cat = p(validation, 'cat')
validation_dog = p(validation, 'dog')
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.models import load_model, Sequential, Model
from tensorflow.keras.layers import Flatten, Dense, Dropout, Conv2D, MaxPooling2D, Input, GlobalMaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator as IDG
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
i = Input(shape=(224, 224, 3))
x = ResNet50(weights='imagenet', include_top=False)(i)
x = GlobalMaxPooling2D()(x)
x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)
x = Dense(2, activation='softmax')(x)
model = Model(inputs=i, outputs=x)
model.get_layer('resnet50').trainable = False
model.summary()
model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])
stop = EarlyStopping(monitor='val_loss', patience=100)
saving = ModelCheckpoint('resnet50.h5', save_weights_only=False, save_best_only=True)
#adjust dataset before training
train_datagen = IDG( rescale = 1.0/255.,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
test_datagen = IDG( rescale = 1.0/255.)
train_generator = train_datagen.flow_from_directory(train,
batch_size=1,
class_mode='categorical',
target_size=(224, 224))
validation_ganerator = test_datagen.flow_from_directory(validation,
batch_size=1,
class_mode='categorical',
target_size=(224, 224))
#Model training
history = model.fit(train_generator,
validation_data=validation_ganerator,
steps_per_epoch=1,
epochs=100,
validation_steps=50,
verbose=1,
callbacks=[stop, saving])
#show results
import numpy as np
import matplotlib.pyplot as plt
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc)) # Get number of epochs
plt.plot ( epochs, acc )
plt.plot ( epochs, val_acc )
plt.title ('Training and validation accuracy')
plt.figure()
plt.plot ( epochs, loss )
plt.plot ( epochs, val_loss )
plt.title ('Training and validation loss')
plt.show()
