From 8bba4c044aa65af9e617096623c78099f3cdaaba Mon Sep 17 00:00:00 2001
From: Gilberto Astolfi <gilbertoastolfi@gmail.com>
Date: Wed, 4 Oct 2017 14:41:06 -0400
Subject: [PATCH] adicionada menu para o keras, criada a classe do keras
---
src/classification/__init__.py | 7 ++
src/classification/cnn_keras.py | 151 ++++++++++++++++++++++++++++++++
2 files changed, 158 insertions(+)
create mode 100644 src/classification/cnn_keras.py
diff --git a/src/classification/__init__.py b/src/classification/__init__.py
index 99cb77b..ab7294e 100644
--- a/src/classification/__init__.py
+++ b/src/classification/__init__.py
@@ -10,6 +10,11 @@ try:
except:
CNNCaffe = None
+try:
+ from .cnn_keras import CNNKeras
+except:
+ CNNKeras = None
+
__all__ = ["cnn_caffe",
"classifier",
"weka_classifiers"]
@@ -22,6 +27,8 @@ from util.config import Config
_classifier_list = OrderedDict( [
["cnn_caffe", Config("Invalid" if CNNCaffe is None else CNNCaffe.__name__,
WekaClassifiers is None and CNNCaffe is not None, bool, meta=CNNCaffe, hidden=CNNCaffe is None)],
+ ["cnn_keras", Config("Invalid" if CNNKeras is None else CNNKeras.__name__,
+ CNNKeras is not None, bool, meta=CNNKeras, hidden=CNNKeras is None)],
["weka_classifiers", Config("Invalid" if WekaClassifiers is None else WekaClassifiers.__name__,
WekaClassifiers is not None, bool, meta=WekaClassifiers, hidden=WekaClassifiers is None)]
] )
diff --git a/src/classification/cnn_keras.py b/src/classification/cnn_keras.py
new file mode 100644
index 0000000..d5e01ba
--- /dev/null
+++ b/src/classification/cnn_keras.py
@@ -0,0 +1,151 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+#
+"""
+ Generic classifier with multiple models
+ Models -> (Xception, VGG16, VGG19, ResNet50, InceptionV3, MobileNet)
+ Name: cnn_keras.py
+ Author: Gabriel Kirsten Menezes (gabriel.kirsten@hotmail.com)
+
+"""
+
+import time
+import os
+from keras import applications
+from keras.preprocessing.image import ImageDataGenerator
+from keras import optimizers
+from keras.models import Model
+from keras.layers import Dropout, Flatten, Dense
+from keras.callbacks import ModelCheckpoint
+from sklearn.metrics import confusion_matrix
+
+from classifier import Classifier
+
+from collections import OrderedDict
+
+from util.config import Config
+from util.file_utils import File
+from util.utils import TimeUtils
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress warnings
+START_TIME = time.time()
+
+# =========================================================
+# Constants
+# =========================================================
+
+IMG_WIDTH, IMG_HEIGHT = 256, 256
+TRAIN_DATA_DIR = "../data/train"
+VALIDATION_DATA_DIR = "../data/validation"
+BATCH_SIZE = 16
+EPOCHS = 50
+CLASS_NAMES = ['ferrugemAsiatica', 'folhaSaudavel',
+ 'fundo', 'manchaAlvo', 'mildio', 'oidio']
+
+class CNNKeras(Classifier):
+
+ def __init__(self):
+ model = applications.VGG16(
+ weights="imagenet", include_top=False, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3))
+
+ for layer in model.layers:
+ layer.trainable = True
+
+ # Adding custom Layers
+ new_custom_layers = model.output
+ new_custom_layers = Flatten()(new_custom_layers)
+ new_custom_layers = Dense(1024, activation="relu")(new_custom_layers)
+ new_custom_layers = Dropout(0.5)(new_custom_layers)
+ new_custom_layers = Dense(1024, activation="relu")(new_custom_layers)
+ predictions = Dense(6, activation="softmax")(new_custom_layers)
+
+ # creating the final model
+ model_final = Model(inputs=model.input, outputs=predictions)
+
+ # compile the model
+ model_final.compile(loss="categorical_crossentropy",
+ optimizer=optimizers.SGD(lr=0.0001, momentum=0.9),
+ metrics=["accuracy"])
+
+ def get_config(self):
+ """Return configuration of classifier.
+
+ Returns
+ -------
+ config : OrderedDict
+ Current configs of classifier.
+ """
+ pass
+
+ def set_config(self, configs):
+ """Update configuration of classifier.
+
+ Parameters
+ ----------
+ configs : OrderedDict
+ New configs of classifier.
+ """
+ pass
+
+ def get_summary_config(self):
+ """Return fomatted summary of configuration.
+
+ Returns
+ -------
+ summary : string
+ Formatted string with summary of configuration.
+ """
+ pass
+
+ def classify(self, dataset, test_dir, test_data):
+ pass
+
+ def train(self, model_final):
+
+ # Initiate the train and test generators with data Augumentation
+ train_datagen = ImageDataGenerator(
+ rescale=1. / 255,
+ horizontal_flip=True,
+ fill_mode="nearest",
+ zoom_range=0.3,
+ width_shift_range=0.3,
+ height_shift_range=0.3,
+ rotation_range=30)
+
+ train_generator = train_datagen.flow_from_directory(
+ TRAIN_DATA_DIR,
+ target_size=(IMG_HEIGHT, IMG_WIDTH),
+ batch_size=BATCH_SIZE,
+ shuffle=True,
+ class_mode="categorical")
+
+ test_datagen = ImageDataGenerator(
+ rescale=1. / 255,
+ horizontal_flip=True,
+ fill_mode="nearest",
+ zoom_range=0.3,
+ width_shift_range=0.3,
+ height_shift_range=0.3,
+ rotation_range=30)
+
+ validation_generator = test_datagen.flow_from_directory(
+ VALIDATION_DATA_DIR,
+ target_size=(IMG_HEIGHT, IMG_WIDTH),
+ batch_size=BATCH_SIZE,
+ shuffle=True,
+ class_mode="categorical")
+
+ # Save the model according to the conditions
+ checkpoint = ModelCheckpoint("../models_checkpoints/" + file_name + ".h5", monitor='val_acc',
+ verbose=1, save_best_only=True, save_weights_only=False,
+ mode='auto', period=1)
+
+ # Train the model
+ model_final.fit_generator(
+ train_generator,
+ steps_per_epoch=train_generator.samples // BATCH_SIZE,
+ epochs=EPOCHS,
+ callbacks=[checkpoint],
+ validation_data=validation_generator,
+ validation_steps=validation_generator.samples // BATCH_SIZE)
+
--
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