cobanov

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation
from keras.optimizers import Adam
from keras.callbacks import TensorBoard
import time

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batch_size = 128
num_classes = 10
epochs = 10
time = time.strftime("%Y_%m_%d_%H_%M_%S")

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(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000,28,28,1)
x_test = x_test.reshape(10000,28,28,1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')

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model = Sequential()

model.add(Conv2D(32, (3,3), padding="same", input_shape=(28,28,1), activation= "relu"))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, activation="relu"))
model.add(Dropout(0.5))

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kerasboard = TensorBoard(log_dir="/tmp/tensorboard/{}".format(time),
                        batch_size=batch_size,
                        histogram_freq=1,
                        write_grads=False)

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

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          validation_split=0.3,
          validation_data=(x_test, y_test),
          callbacks=[kerasboard])

cobanov

# -*- coding: utf-8 -*- 

"""
Deep Learning Türkiye topluluğu için Mert Çobanoğlu tarafından hazırlanmıştır.
Amaç: Keras ile nesne tanıma.
Algoritma: Evrişimli Sinir Ağları (Convolutional Neural Networks)
Ek: Çalışma ile ilgili rehber README.md dosyasında belirtilmiştir.
"""

from keras.applications.vgg16 import VGG16

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from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input, decode_predictions
import numpy as np

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model = VGG16(weights='imagenet')

img_path = 'images/bird.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

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preds = model.predict(x)

print('Predicted:', decode_predictions(preds, top=3)[0])