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September 13, 2023 03:34
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Updated to the Keras 2.0 API.
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'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats | |
- put the cat pictures index 1000-1400 in data/validation/cats | |
- put the dogs pictures index 12500-13499 in data/train/dogs | |
- put the dog pictures index 13500-13900 in data/validation/dogs | |
So that we have 1000 training examples for each class, and 400 validation examples for each class. | |
In summary, this is our directory structure: | |
``` | |
data/ | |
train/ | |
dogs/ | |
dog001.jpg | |
dog002.jpg | |
... | |
cats/ | |
cat001.jpg | |
cat002.jpg | |
... | |
validation/ | |
dogs/ | |
dog001.jpg | |
dog002.jpg | |
... | |
cats/ | |
cat001.jpg | |
cat002.jpg | |
... | |
``` | |
''' | |
import numpy as np | |
from keras.preprocessing.image import ImageDataGenerator | |
from keras.models import Sequential | |
from keras.layers import Dropout, Flatten, Dense | |
from keras import applications | |
# dimensions of our images. | |
img_width, img_height = 150, 150 | |
top_model_weights_path = 'bottleneck_fc_model.h5' | |
train_data_dir = 'data/train' | |
validation_data_dir = 'data/validation' | |
nb_train_samples = 2000 | |
nb_validation_samples = 800 | |
epochs = 50 | |
batch_size = 16 | |
def save_bottlebeck_features(): | |
datagen = ImageDataGenerator(rescale=1. / 255) | |
# build the VGG16 network | |
model = applications.VGG16(include_top=False, weights='imagenet') | |
generator = datagen.flow_from_directory( | |
train_data_dir, | |
target_size=(img_width, img_height), | |
batch_size=batch_size, | |
class_mode=None, | |
shuffle=False) | |
bottleneck_features_train = model.predict_generator( | |
generator, nb_train_samples // batch_size) | |
np.save(open('bottleneck_features_train.npy', 'w'), | |
bottleneck_features_train) | |
generator = datagen.flow_from_directory( | |
validation_data_dir, | |
target_size=(img_width, img_height), | |
batch_size=batch_size, | |
class_mode=None, | |
shuffle=False) | |
bottleneck_features_validation = model.predict_generator( | |
generator, nb_validation_samples // batch_size) | |
np.save(open('bottleneck_features_validation.npy', 'w'), | |
bottleneck_features_validation) | |
def train_top_model(): | |
train_data = np.load(open('bottleneck_features_train.npy')) | |
train_labels = np.array( | |
[0] * (nb_train_samples / 2) + [1] * (nb_train_samples / 2)) | |
validation_data = np.load(open('bottleneck_features_validation.npy')) | |
validation_labels = np.array( | |
[0] * (nb_validation_samples / 2) + [1] * (nb_validation_samples / 2)) | |
model = Sequential() | |
model.add(Flatten(input_shape=train_data.shape[1:])) | |
model.add(Dense(256, activation='relu')) | |
model.add(Dropout(0.5)) | |
model.add(Dense(1, activation='sigmoid')) | |
model.compile(optimizer='rmsprop', | |
loss='binary_crossentropy', metrics=['accuracy']) | |
model.fit(train_data, train_labels, | |
epochs=epochs, | |
batch_size=batch_size, | |
validation_data=(validation_data, validation_labels)) | |
model.save_weights(top_model_weights_path) | |
save_bottlebeck_features() | |
train_top_model() |
Fine tuned models' Prediction code
This codes were checked by myself. They all worked fine.
- If someone want to predict image classes in same model script where model were trained, here is the code :
img_width, img_height = 224, 224 batch_size = 1 datagen = ImageDataGenerator(rescale=1. / 255) test_generator = datagen.flow_from_directory( test_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode=None, shuffle=False) test_generator.reset() pred= model.predict_generator(test_generator, steps = no_of_images/batch_size) predicted_class_indices=np.argmax(pred, axis =1 ) labels = (train_generator.class_indices) labels = dict((v, k) for k, v in labels.items()) predictions = [labels[k] for k in predicted_class_indices] print(predicted_class_indices) print (labels) print (predictions)
This code is inspired by stack overflow answer. click here
- If someone want to predict image classes in different script (separate from training script file), here is the code :
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator import json import os from tensorflow.keras.models import model_from_json #Just give below lines parameters best_weights = 'path to .h5 weight file' model_json = 'path to saved model json file' test_dir = 'path to test images' img_width, img_height = 224, 224 batch_size = 1 nb_img_samples = #no of testing images with open(model_json, 'r') as json_file: json_savedModel= json_file.read() model = tf.keras.models.model_from_json(json_savedModel) model.summary() model.load_weights(best_weights) datagen = ImageDataGenerator(rescale=1. / 255) test_generator = datagen.flow_from_directory( folder_path, target_size=(img_width, img_height), batch_size=batch_size, class_mode=None, shuffle=False) test_generator.reset() pred= model.predict_generator(test_generator, steps = nb_img_samples/batch_size) predicted_class_indices=np.argmax(pred,axis=1) labels = {'cats': 0, 'dogs': 1} #if you have more classes, just add like this in correct order where your training folder order. labels = dict((v,k) for k,v in labels.items()) predictions = [labels[k] for k in predicted_class_indices] print(predicted_class_indices) print (labels) print (predictions)
please help me, I got this error when running that codes, I want to predict image classes in same model script
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Yes, because if we provide equal no of images then the model would be able to generalize well