/Python/Cross-validation sklearn image classification

Cross-validation sklearn image classification

import numpy as np
import tensorflow as tf
from keras.models import Model
from keras.layers import Input, Activation, Dense, Conv2D, MaxPooling2D, Flatten
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
from keras.callbacks import TensorBoard

# Images Dimensions
img_width, img_height = 200, 200

# Data Path
train_data_dir = 'data/train'
validation_data_dir = 'data/validation'

# Parameters
nb_train_samples = 100
nb_validation_samples = 50
epochs = 50
batch_size = 10

# TensorBoard Callbacks
callbacks = TensorBoard(log_dir='./Graph')

# Training Data Augmentation
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

# Rescale Testing Data
test_datagen = ImageDataGenerator(rescale=1. / 255)

# Train Data Generator
train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

# Testing Data Generator
validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

# Feature Extraction Layer KorNet
inputs = Input(shape=(img_width, img_height, 3))
conv_layer = Conv2D(16, (5, 5), strides=(3,3), activation='relu')(inputs) 
conv_layer = MaxPooling2D((2, 2))(conv_layer) 
conv_layer = Conv2D(32, (5, 5), strides=(3,3), activation='relu')(conv_layer) 
conv_layer = MaxPooling2D((2, 2))(conv_layer) 

# Flatten Layer
flatten = Flatten()(conv_layer) 

# Fully Connected Layer
fc_layer = Dense(32, activation='relu')(flatten)
outputs = Dense(3, activation='softmax')(fc_layer)

model = Model(inputs=inputs, outputs=outputs)

# Adam Optimizer and Cross Entropy Loss
adam = Adam(lr=0.0001)
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])

# Print Model Summary
print(model.summary())

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size, 
    callbacks=[callbacks])

model.save('./models/model.h5')
model.save_weights('./models/weights.h5')

Source: stackoverflow.com

Similar codes

210 votes391 views6 answers
Looping over dictionary python
Python
20 votes209 views2 answers
Cv2 not found
Python
1 vote201 views1 answer
Selenium python find all links
Python
1 vote222 views1 answer
Set size of button tkinter
Python
31 votes182 views5 answers
Python day from date
Python
-1 vote178 views1 answer
Use linear regression to predict python
Python
5 votes174 views2 answers
Pandas convert index to column
Python
0 vote221 views1 answer
Sorting-a-python-list-by-two-fields
Python
6 votes176 views3 answers
Pandas drop row by condition
Python
0 vote188 views1 answer
Comment voir les requêtes SQL brutes exécutées par Django?
Python