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Minor modification to remove pylint messages

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This commit is contained in:
Heiko J Schick
2020-10-08 16:43:50 +02:00
parent 1db90f8a0a
commit d081002d5e
3 changed files with 94 additions and 93 deletions
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linear_regression.py
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"""
Simple linear regression application.
Simple example on how to fine tune models in Keras and how to use them.
Part 2
Source: https://www.guru99.com/keras-tutorial.html
"""
prepare_train_model.py
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# Source: https://www.guru99.com/keras-tutorial.html
"""
Simple example on how to fine tune models in Keras and how to use them.
Part 1
Source: https://www.guru99.com/keras-tutorial.html
"""
#### Data preparation
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Flatten, Dense
from keras.applications.vgg16 import VGG16
train_path = 'images/train/'
test_path = 'images/test/'
batch_size = 16
image_size = 224
num_class = 8
TRAIN_PATH = 'images/train/'
TEST_PATH = 'images/test/'
BATCH_SIZE = 16
IMAGE_SIZE = 224
NUM_CLASSES = 8
# The ImageDataGenerator will make an X_training data from a directory.
# The sub-directory in that directory will be used as a class for each object.
# The image will be loaded with the RGB color mode, with the categorical class
# mode for the Y_training data, with a batch size of 16. Finally, shuffle the
# data.
'''
The ImageDataGenerator will make an X_training data from a directory.
The sub-directory in that directory will be used as a class for each object.
The image will be loaded with the RGB color mode, with the categorical class
mode for the Y_training data, with a batch size of 16. Finally, shuffle the
data.
'''
train_datagen = ImageDataGenerator(validation_split=0.3,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
train_generator = train_datagen.flow_from_directory(
directory=train_path,
target_size=(image_size,image_size),
batch_size=batch_size,
directory=TRAIN_PATH,
target_size=(IMAGE_SIZE,IMAGE_SIZE),
batch_size=BATCH_SIZE,
class_mode='categorical',
color_mode='rgb',
shuffle=True)
'''
Let's see our images randomly by plotting them with matplotlib
'''
x_batch, y_batch = train_generator.next()
# Let's see our images randomly by plotting them with matplotlib
# x_batch, y_batch = train_generator.next()
x_batch, y_batch = next(train_generator)
fig=plt.figure()
columns = 4
rows = 4
for i in range(1, columns*rows):
num = np.random.randint(batch_size)
COLUMNS = 4
ROWS = 4
for i in range(1, COLUMNS*ROWS):
num = np.random.randint(BATCH_SIZE)
image = x_batch[num].astype(np.int)
fig.add_subplot(rows, columns, i)
fig.add_subplot(ROWS, COLUMNS, i)
plt.imshow(image)
plt.show()
### Creating model
import keras
from keras.models import Model, load_model
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.vgg16 import VGG16
'''
Let's create our network model from VGG16 with imageNet pre-trained weight.
We will freeze these layers so that the layers are not trainable to help us
reduce the computation time.
'''
# Let's create our network model from VGG16 with imageNet pre-trained weight.
# We will freeze these layers so that the layers are not trainable to help us
# reduce the computation time.
# Load the VGG model
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(image_size, image_size, 3))
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))
print(base_model.summary())
# Freeze the layers
# Freeze the layers
for layer in base_model.layers:
layer.trainable = False
# Create the model
model = keras.models.Sequential()
# Add the vgg convolutional base model
model.add(base_model)
# Add new layers
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(1024, activation='relu'))
model.add(Dense(num_class, activation='softmax'))
# Show a summary of the model. Check the number of trainable parameters
model.add(Dense(NUM_CLASSES, activation='softmax'))
# Show a summary of the model. Check the number of trainable parameters
print(model.summary())
input("Press Enter to continue...")
'''
As you can see, the summary of our network model. From an input from
VGG16 Layers, then we add 2 Fully Connected Layer which will extract 1024
features and an output layer that will compute the 8 classes with the softmax
activation.
'''
# As you can see, the summary of our network model. From an input from
# VGG16 Layers, then we add 2 Fully Connected Layer which will extract 1024
# features and an output layer that will compute the 8 classes with the softmax
# activation.
#### Training
#### Training
# Compile the model
from keras.optimizers import SGD
# from keras.optimizers import SGD
model.compile(loss='categorical_crossentropy',
optimizer=SGD(lr=1e-3),
metrics=['accuracy'])
# model.compile(loss='categorical_crossentropy',
# optimizer=SGD(lr=1e-3),
# metrics=['accuracy'])
'''
# Start the training process
model.fit(x_train, y_train, validation_split=0.30, batch_size=32, epochs=50, verbose=2)
# model.fit(x_train, y_train, validation_split=0.30, batch_size=32, epochs=50, verbose=2)
# Save the model
model.save('catdog.h5')
'''
# model.save('catdog.h5')
history = model.fit_generator(train_generator,
steps_per_epoch=train_generator.n/batch_size,
steps_per_epoch=train_generator.n/BATCH_SIZE,
epochs=10)
model.save('fine_tune.h5')
# summarize history for accuracy
import matplotlib.pyplot as plt
# Summarize history for accuracy
plt.plot(history.history['loss'])
plt.title('loss')
plt.ylabel('loss')
@@ -123,8 +118,7 @@ plt.xlabel('epoch')
plt.legend(['loss'], loc='upper left')
plt.show()
'''
As you can see, our losses are dropped significantly and the accuracy is
almost 100%. For testing our model, we randomly picked images over the internet
and put it on the test folder with a different class to test
'''
# Our losses are dropped significantly and the accuracy is almost 100%. For
# testing our model, we randomly picked images over the internet and put it on
# the test folder with a different class to test
test_model.py
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@@ -1,5 +1,11 @@
import keras
from keras.models import Model, load_model
"""
Simple example on how to fine tune models in Keras and how to use them.
Part 1
Source: https://www.guru99.com/keras-tutorial.html
"""
from keras.models import load_model
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import numpy as np
@@ -9,40 +15,45 @@ train_datagen = ImageDataGenerator(validation_split=0.3,
zoom_range=0.2,
horizontal_flip=True)
train_path = 'images/train/'
test_path = 'images/test/'
batch_size = 16
image_size = 224
num_class = 8
TRAIN_PATH = 'images/train/'
TEST_PATH = 'images/test/'
BATCH_SIZE = 16
IMAGE_SIZE = 224
NUM_CLASSES = 8
### Testing our model
model = load_model('fine_tune.h5')
test_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_directory(
directory=train_path,
target_size=(image_size,image_size),
batch_size=batch_size,
directory=TRAIN_PATH,
target_size=(IMAGE_SIZE, IMAGE_SIZE),
batch_size=BATCH_SIZE,
class_mode='categorical',
color_mode='rgb',
shuffle=True)
test_generator = test_datagen.flow_from_directory(
directory=test_path,
target_size=(image_size, image_size),
directory=TEST_PATH,
target_size=(IMAGE_SIZE, IMAGE_SIZE),
color_mode='rgb',
shuffle=False,
class_mode='categorical',
batch_size=1)
filenames = test_generator.filenames
filenames = test_generator.filenames
nb_samples = len(filenames)
fig=plt.figure()
columns = 4
rows = 4
for i in range(1, columns*rows -1):
x_batch, y_batch = test_generator.next()
fig = plt.figure()
COLUMNS = 4
ROWS = 4
# And our test is as given below! Only 1 image is predicted wrong from a test of
# 14 images!
for i in range(1, COLUMNS*ROWS -1):
x_batch, y_batch = next(test_generator)
name = model.predict(x_batch)
name = np.argmax(name, axis=-1)
@@ -55,12 +66,7 @@ for i in range(1, columns*rows -1):
true_value = [label_map[k] for k in true_name]
image = x_batch[0].astype(np.int)
fig.add_subplot(rows, columns, i)
fig.add_subplot(ROWS, COLUMNS, i)
plt.title(str(predictions[0]) + ':' + str(true_value[0]))
plt.imshow(image)
plt.show()
'''
And our test is as given below! Only 1 image is predicted wrong from a test of
14 images!
'''