FlowerRecognition/class.py at main · n4hm3/FlowerRecognition · GitHub

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from utils import load_data
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

(feature,labels) = load_data()

x_train, x_test, y_train, y_test = train_test_split(feature, labels, test_size=0.1)

cat = ['daisy', 'dandelion','rose','sunflower','tulip']

input_layer = tf.keras.layers.Input([125,125,3])

conv1 = tf.keras.layers.Conv2D(filters = 32, kernel_size=(5,5),padding='same',
    activation = 'relu')(input_layer)
pool1 = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv1)

conv2 = tf.keras.layers.Conv2D(filters=64,kernel_size=(3,3),padding='same',
    activation='relu')(pool1)
pool2 = tf.keras.layers.MaxPooling2D(pool_size=(2,2),strides=(2,2))(conv2)

conv3 = tf.keras.layers.Conv2D(filters=96,kernel_size=(3,3),padding='same',
    activation='relu')(pool2)
pool3 = tf.keras.layers.MaxPooling2D(pool_size=(2,2),strides=(2,2))(conv3)

conv4 = tf.keras.layers.Conv2D(filters=96,kernel_size=(3,3),padding='same',
    activation='relu')(pool3)
pool4 = tf.keras.layers.MaxPooling2D(pool_size=(2,2),strides=(2,2))(conv4)

flt1 = tf.keras.layers.Flatten()(pool4)
dn1 = tf.keras.layers.Dense(512,activation='relu')(flt1)
out = tf.keras.layers.Dense(5,activation='softmax')(dn1)

model = tf.keras.Model(input_layer,out)

model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',
    metrics=['accuracy'])

model.fit(x_train,y_train, batch_size = 100, epochs =10)
model.save('mod.h5')