s464931/Sztuczna_Inteligencja_Gr16
1
0
Fork 0
forked from s452624/Sztuczna_Inteligencja_Gr16
Code Issues Pull Requests Projects Releases Wiki Activity

Zaktualizuj 'neural_network.py'

Browse Source 
poprawka sieci neuronowych
This commit is contained in:
Aniela Walczak 2022-05-26 16:05:08 +02:00
parent c959093e38
commit 32179be076
1 changed files with 30 additions and 33 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

45
neural_network.py
View File

@ -3,29 +3,29 @@ import cv2
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import random
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=3)
model.save('handwritten.model')
model = tf.keras.models.load_model('handwritten.model')
def recognition():
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.compile(optimizer ='adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
model.fit(x_train, y_train, epochs = 3)
model.save('handwritten.model')
model = tf.keras.models.load_model('handwritten.model')
image_number = 1
while os.path.isfile(f"digits/digit{image_number}.png"):
image_number = random.randint(1, 9)
try:
img = cv2.imread(f"digits/digit{image_number}.png")[:,:,0]
img = np.invert(np.array([img]))
@ -35,12 +35,9 @@ def recognition():
plt.show()
except:
print("Error!")
finally:
image_number +=1
loss, accuracy = model.evaluate(x_test, y_test)
print(loss)
print(accuracy)
recognition()