I have trained an image classifier using keras and it gave a very good accuracy. I've saved the model using the save()and saved it using the h5 format. How can I make a prediction using the model?
The code is:
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 5,
validation_data = test_set,
validation_steps = 2000)
classifier.save('classifier.h5')
Thanks in Advance..!!
The first step is to import your model using load_model method.
from keras.models import load_model
model = load_model('my_model.h5')
Then you have to compile the model in order to make predictions.
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
Now you can predict results for a new entry image.
from keras.preprocessing import image
test_image = image.load_img(imagePath, target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
#predict the result
result = model.predict(test_image)
Related
My problem is that I am trying to train a convolution neural network with Keras in google colab that is able to distinguish between dogs and cats, but at the time of passing to the training phase my model takes a long time to train and I wanted to know how I can use the GPU in the right way in order to make the training time take less time.
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('/content/drive/MyDrive/Colab Notebooks/files/dataset_CNN/training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('/content/drive/MyDrive/Colab Notebooks/files/dataset_CNN/test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
raise SystemError('GPU device not found')
with tf.device('/device:GPU:0'):#I tried to put this part that I found by researching on the internet
classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.fit(training_set,
steps_per_epoch = 8000,
epochs = 25,
validation_data = test_set,
validation_steps = 2000)
Previously, I did not put this part of the code "with tf.device('/device:GPU:0')", I saw this part of the code in an example on the internet, but it is still slow when training. I already checked the Available GPUs using:
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
raise SystemError('GPU device not found')
To Select GPU in Google Colab -Select Edit - Notebook Setting - Hardware accelerator - GPU - Save
ImageDataGenerator is not recommended for new code. Instead you can use these augmentation features directly through layers in model training as below:
classifier = tf.keras.Sequential([
#data augmention layers
layers.Resizing(IMG_SIZE, IMG_SIZE),
layers.Rescaling(1./255),
layers.RandomFlip("horizontal"),
layers.RandomZoom(0.1),
#model building layers
layers.Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'),
layers.MaxPooling2D(pool_size = (2, 2)),
layers.Conv2D(32, (3, 3), activation = 'relu'),
layers.MaxPooling2D(pool_size = (2, 2)),
layers.Flatten(),
layers.Dense(units = 128, activation = 'relu'),
layers.Dense(units = 1, activation = 'sigmoid')
])
Also, you should use image_dataset_from_directory to import the images dataset which generates a tf.data.Dataset from image files in a directory. Please refer to this gist of replicated code.
Note:
fit() automatically calculates Steps_per_epoch from the training set as per batch_size.Steps_per_epoch = len(training_dataset)//batch_size
I am new to ML and I am trying to play a little with this tutorial:
https://medium.com/#ferhat00/deep-learning-with-keras-classifying-cats-and-dogs-part-2-21b3b25bbe5c . Anyway, the CNN seems to work only in the case I don't save it in an h5 file after the training. More precisely:
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout
from keras import optimizers
classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (128, 128, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(64, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units = 64, activation = 'relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('PetImages/training_set',
target_size = (128, 128),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('PetImages/test_set',
target_size = (128, 128),
batch_size = 32,
class_mode = 'binary')
history = classifier.fit_generator(training_set,
steps_per_epoch = 8000/32,
epochs = 30,
validation_data = test_set,
validation_steps = 2000/32, workers=12, max_q_size=100)
///////////////////////////////////////
classifier.save("cnnPetsModel.h5")
print("saved model to disk")
///////////////////////////////////////
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np
img_path = 'PetImages/test_set/Dog/5000.jpg'
img = image.load_img(img_path, target_size=(128, 128))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = classifier.predict(x)
training_set.class_indices
print(preds)
if preds[0][0] == 1:
prediction = 'dog'
else:
prediction = 'cat'
print(prediction)
img_path = 'PetImages/test_set/Cat/5000.jpg'
img = image.load_img(img_path, target_size=(128, 128))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = classifier.predict(x)
training_set.class_indices
print(preds)
if preds[0][0] == 1:
prediction = 'dog'
else:
prediction = 'cat'
print(prediction)
If I remove the code between "////////", the two images are predicted correctly, while it becomes always "cat" in the case I save the model. Why this happens? I am only saving parameters, structure etc. in a file, at all...
Thanks in advance!
EDIT:
I moved the saving at the end of the code and the model predicts correctly. Anyway, if such a model is loaded from another python file and then used for predict(x) like above, it results in an untrained one which always returns "cat".
I am training a Keras model for multi-label image classification, i.e. 3 classes namely flood, wildfire, storm.
But I am getting only [[1.]] instead of something like [0 0 1]. So if third bit is one, its a storm. But I don't know why it's returning just a single value [[1.]].
# # Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
import numpy as np
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def create_model() :
# Initialising the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
return classifier
def train_save_model():
classifier = create_model()
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Part 2 - Fitting the CNN to the images
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('validation_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 1407,
epochs = 1,
validation_data = test_set,
validation_steps = 100)
classifier.save_weights("model.h5")
# Part 3 - Making new predictions
def test_model():
classifier = create_model()
classifier.load_weights("model.h5")
test_image = image.load_img('validation_set/tornado/110.jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
# print(test_image)
result = classifier.predict(test_image)
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True,
)
training_set = train_datagen.flow_from_directory('training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
training_set.class_indices
# print(training_set.class_indices)
print(result)
train_save_model()
test_model()
result = classifier.predict(test_image)
I tried printing this result variable and I get [[1.]]. I cannot understand at all how's that happening.
If you have N labels, then the last layer (i.e. the sigmoid classifier layer) must also have N neurons, one for each of the classes:
classifier.add(Dense(units=3, activation='sigmoid'))
Then the output of the model, for each input sample, would be 3 numbers corresponding to three labels.
Update: Remove the class_mode = 'binary' from all flow_from_directory calls. That's because you are doing classification among multiple classes and therefore the generated labels should be either categorical (default behavior) or sparse (i.e. class_mode='sparse'). Further, after reading the relevant parts of your code, it seems that you are doing multi-class classification, and not multi-label classification. Read this answer to make sure and also to find out which activation and loss function you should use.
As loss function use categorical_crossentropy instead of binary_crossentropy.
I want to predict the kind of 2 diseases but I get results as binary (like 1.0 and 0.0). How can I get accuracy of these (like 0.7213)?
Training code:
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
# Intialising the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Part 2 - Fitting the CNN to the images
import h5py
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 100,
epochs = 1,
validation_data = test_set,
validation_steps = 100)
Single prediction code:
import numpy as np
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img,image
test_image = image.load_img('path_to_image', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
print(result[0][0]) # Prints 1.0 or 0.0
# I want accuracy rate for this prediction like 0.7213
The file structures is like:
test_set
benigne
benigne_images
melignant
melignant_images
training set
Training set structure is also the same as test set.
Update: As you clarified in the comments, you are looking for the probabilities of each class given one single test sample. Therefore you can use predict method. However, note that you must first preprocess the image the same way you have done in the training phase:
test_image /= 255.0
result = classifier.predict(test_image)
The result would be the probability of the given image belonging to class one (i.e. positive class).
If you have a generator for test data, then you can use evaluate_generator() to get the loss as well as the accuracy (or any other metric you have set) of the model on the test data.
For example, right after fitting the model, i.e. using fit_generator, you can use evaluate_generator on your test data generator, i.e. test_set:
loss, acc = evaluate_generator(test_set)
I am using Convolutional Neural Networking for vehicle identification, my first time. Currently, I am working with just 2 classes(bike and car). Training set: 420 car images and 825 bike images. Test set: 44 car images and 110 bike images Car and Bike images are in different format(bmp,jpg). In single prediction, I am always getting 'bike'. I have tried using the Sigmoid function in the output layer. Then I get only 'car'. My code is like following: ``
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense,Dropout
classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (128, 128, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (3, 3)))
# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (3, 3)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dropout(0.3))
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Part 2 - Fitting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
rotation_range= 3,
fill_mode = 'nearest',
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
rotation_range= 3,
fill_mode = 'nearest',
horizontal_flip = True)
training_set = train_datagen.flow_from_directory('dataset/training_set',
target_size = (128, 128),
batch_size = 10,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('dataset/test_set',
target_size = (128, 128),
batch_size = 10,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 1092//10,
epochs = 3,
validation_data = test_set,
validation_steps = 20)
classifier.save("car_bike.h5")
And I wanted to test a single image like the following:
test_image = image.load_img('dataset/single_prediction/download (3).jpg', target_size = (128, 128))
test_image = image.img_to_array(test_image)
test_image *= (1/255.0)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
if result[0][0] == 1:
prediction = 'bike'
else:
prediction = 'car'
print(" {}".format(prediction))
If you print your result matrix you'll see that it doesn't have only 1s and 0s but floats between these numbers. You may pick a threshold and set values that exceed it to 1 and everything else to 0.