TensorflowMachineLearningFlowerClassifier/flowerclassifier.py at main · GeraldWambui/TensorflowMachineLearningFlowerClassifier · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
import tensorflow as tf
import numpy as np
import os
import glob
import shutil
import matplotlib.pyplot as plt

from tensorflow.keras.preprocessing.image import ImageDataGenerator

#data loading
_URL = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz/"
zip_file = tf.keras.utils.get_file(origin=_URL,
                                   fname="flower_photos.tgz",
                                   extract=True)
base_dir = os.path.join(os.path.dirname(zip_file), 'flower_photos')

#creating labels
classes = ['roses', 'daisy', 'dandelion', 'sunflowers', 'tulips']

#for cl in classes
for cl in classes:
  img_path = os.path.join(base_dir, cl)
  images = glob.glob(img_path + '/*.jpg')
  print("{}: {} Images".format(cl, len(images)))
  train, val = images[:round(len(images)*0.8)], images[round(len(images)*0.8):]

  for t in train:
    if not os.path.exists(os.path.join(base_dir, 'train', cl)):
      os.makedirs(os.path.join(base_dir, 'train', cl))
    shutil.move(t, os.path.join(base_dir, 'train', cl))

  for v in val:
    if not os.path.exists(os.path.join(base_dir, 'val', cl)):
      os.makedirs(os.path.join(base_dir, 'val', cl))
    shutil.move(v, os.path.join(base_dir, 'val', cl))

train_dir = os.path.join(base_dir, 'train')
val_dir = os.path.join(base_dir, 'val')

#data augmentation

batch_size = 100
IMG_SHAPE = 150

#random horizontal flip
image_gen = ImageDataGenerator(rescale=1./255, horizontal_flip=True)
train_data_gen = image_gen.flow_from_directory(batch_size=batch_size,
                                               directory=train_dir,
                                               shuffle=True,
                                               target_size=(IMG_SHAPE, IMG_SHAPE))

#fun to plot images
#This function will plot images in the form of a grid with 1 row and 5 columns where images are placed in each column.
def plotImages(images_arr):
    fig, axes = plt.subplots(1, 5, figsize=(20,20))
    axes = axes.flatten()
    for img, ax in zip( images_arr, axes):
        ax.imshow(img)
    plt.tight_layout()
    plt.show()


augmented_images = [train_data_gen[0][0][0] for i in range(5)]
plotImages(augmented_images)

#random rotation
image_gen = ImageDataGenerator(rescale=1./255, rotation_range=45)
train_data_gen = image_gen.flow_from_directory(batch_size=batch_size,
                                               directory=train_dir,
                                               shuffle=True,
                                               target_size=(IMG_SHAPE, IMG_SHAPE))
augmented_images = [train_data_gen[0][0][0]for i in range(5)]
plotImages(augmented_images)

#random zoom
image_gen = ImageDataGenerator(rescale=1./255, zoom_range=0.5)
train_data_gen=image_gen.flow_from_directory(batch_size=batch_size,
                                             directory=train_dir,
                                             shuffle=True,
                                             target_size=(IMG_SHAPE, IMG_SHAPE))
augmented_images = [train_data_gen[0][0][0] for i in range(5)]
plotImages(augmented_images)

#put it all together
image_gen_train = ImageDataGenerator(
    rescale=1./255,
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)
train_data_gen = image_gen_train.flow_from_directory(batch_size=batch_size,
                                                     directory=train_dir,
                                                     shuffle=True,
                                                     target_size=(IMG_SHAPE, IMG_SHAPE),
                                                     class_mode='sparse')
augmented_images = [train_data_gen[0][0][0] for i in range(5)]
plotImages(augmented_images)

image_gen_val = ImageDataGenerator(rescale=1./255)
val_data_gen = image_gen_val.flow_from_directory(batch_size=batch_size,
                                                 directory=val_dir,
                                                 target_size=(IMG_SHAPE, IMG_SHAPE),
                                                 class_mode='sparse')

#cnn model creation
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), action='relu', input_shape=(150, 150, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(2)
])

#compile the model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
#Train the model
epochs = 80
history = model.fit_generator(
    train_data_gen,
    steps_per_epoch=int(np.ceil(train_data_gen.n / float(batch_size))),
    epochs=epochs,
    validation_data=val_data_gen,
    validation_steps=int(np.ceil(val_data_gen.n / float(batch_size)))
)

#plot training and validation graphs using pyplot
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(epochs)