I downloaded the mnist dataset (jpg) and created a model file with .hdf (.h5).
Images were recognized using the .h5 model, but the recognition rate is low..
.
The accuracy is low when actual compile..
Did I do something wrong?.
.
The image used the 28x28 image of the grayscale..
import os
import cv2
import numpy as np
import tensorflow as tf
from PIL import Image
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
import random
FILENAME = 'model.h5'
WIDTH = 28
HEIGHT = 28
EPOCHES = 100
BATCH_SIZE = 32
def create_dataset(img_folder):
img_data_array=[]
class_name=[]
for path in os.listdir(img_folder):
if path == ".DS_Store":
continue
for file in os.listdir(os.path.join(img_folder, path)):
if file == ".DS_Store":
continue
image_path = os.path.join(img_folder, path, file)
image = cv2.imread( image_path, cv2.IMREAD_UNCHANGED)
image = cv2.resize(image, (HEIGHT, WIDTH),interpolation = cv2.INTER_AREA)
image = np.array(image)
image = image.astype('float32')
image /= 255
img_data_array.append(image)
class_name.append(path)
return img_data_array, class_name
img_data, class_name = create_dataset(r'/Users/animalman/Documents/test/grayscale/train')
test, test_class_name = create_dataset(r'/Users/animalman/Documents/test/grayscale/test')
target_dict = {k: v for v, k in enumerate(np.unique(class_name))}
target_val = [target_dict[class_name[i]] for i in range(len(class_name))]
test_dict = {k: v for v, k in enumerate(np.unique(test_class_name))}
test_val = [test_dict[test_class_name[i]] for i in range(len(test_class_name))]
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# tensor
history = model.fit(x=tf.cast(np.array(img_data), tf.float64), y=tf.cast(list(map(int,target_val)),tf.int32), epochs=EPOCHES, batch_size=BATCH_SIZE, validation_split=0.33)
evaluate = model.evaluate(x=tf.cast(np.array(img_data), tf.float64), y=tf.cast(list(map(int,target_val)),tf.int32), batch_size=BATCH_SIZE)
print('Train:', evaluate)
test_evaluate = model.evaluate(x=tf.cast(np.array(test), tf.float64), y=tf.cast(list(map(int,test_val)),tf.int32), batch_size=BATCH_SIZE)
print('Test:', test_evaluate)
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
test_loss, test_acc = model.evaluate(x_test, y_test)
print('mnist', test_acc)
model.save(FILENAME)
...
...
Epoch 98/100
1257/1257 [==============================] - 3s 2ms/step - loss: 5.5190e-08 - accuracy: 1.0000 - val_loss: 43.3440 - val_accuracy: 0.1135
Epoch 99/100
1257/1257 [==============================] - 3s 2ms/step - loss: 4.0746e-08 - accuracy: 1.0000 - val_loss: 43.3764 - val_accuracy: 0.1136
Epoch 100/100
1257/1257 [==============================] - 3s 2ms/step - loss: 2.3033e-08 - accuracy: 1.0000 - val_loss: 43.4628 - val_accuracy: 0.1136
..
..
Train: [14.343465805053711, 0.7074833512306213]
313/313 [==============================] - 0s 579us/step - loss: 14.7582 - accuracy: 0.6990
Test: [14.758186340332031, 0.6990000009536743]
313/313 [==============================] - 0s 850us/step - loss: 3887.2236 - accuracy: 0.6991
mnist : 0.6991000175476074
