I'm beginner with deep learning and keras/tensorflow. I have followed the first tutorial on tensorflow.org: a basic classification with fashion MNIST.
In this case the input data are 60000, 28x28 images and the model is this:
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax)
])
Compiled with:
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
At the end of training the model has this accuracy:
10000/10000 [==============================] - 0s 21us/step
Test accuracy: 0.8769
It's ok. Now I'm trying to duplicate this model with another set of datas. New input is a dataset downloaded from kaggle.
The dataset has images with different sized of dogs and cats, so I have create a simple script that get the images, resize in 28x28 pixel and convert in a numpy array.
This is the code to do this:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from tensorflow.keras.models import load_model
from PIL import Image
import os
# Helper libraries
import numpy as np
# base path dataset
base_path = './dataset/'
training_path = base_path + "training_set/"
test_path = base_path + "test_set/"
# size rate of images
size = 28, 28
#
train_images = []
train_labels = []
test_images = []
test_labels = []
classes = ['dogs', 'cats']
# Scorre sulle cartelle contenute nel path e trasforma le immagini in nparray
def from_files_to_nparray(path):
images = []
labels = []
for subfolder in os.listdir(path):
if subfolder == '.DS_Store':
continue
for image_name in os.listdir(path + subfolder):
if not image_name.endswith('.jpg'):
continue
img = Image.open(path + subfolder + "/" + image_name).convert("L").resize(size) # convert to grayscale and resize
npimage = np.asarray(img)
images.append(npimage)
labels.append(classes.index(subfolder))
img.close()
# convertt to np arrays
images = np.asarray(images)
labels = np.asarray(labels)
# Normalize to [0, 1]
images = images / 255.0
return (images, labels)
(train_images, train_labels) = from_files_to_nparray(training_path)
(test_images, test_labels) = from_files_to_nparray(test_path)
At the end I have these shapes:
Train images shape : (8000, 128, 128)
Labels images shape : (8000,)
Test images shape : (2000, 128, 128)
Test images shape : (2000,)
After training the same model (but with the last dense layer format by 2 neurons) I have this result, that should be ok:
Train images shape : (8000, 28, 28)
Labels images shape : (8000,)
Test images shape : (2000, 28, 28)
Test images shape : (2000,)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 784) 0
_________________________________________________________________
dense (Dense) (None, 128) 100480
_________________________________________________________________
dense_1 (Dense) (None, 2) 258
=================================================================
Total params: 100,738
Trainable params: 100,738
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/5
2018-07-27 15:25:51.283117: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
8000/8000 [==============================] - 1s 66us/step - loss: 0.6924 - acc: 0.5466
Epoch 2/5
8000/8000 [==============================] - 0s 39us/step - loss: 0.6679 - acc: 0.5822
Epoch 3/5
8000/8000 [==============================] - 0s 41us/step - loss: 0.6593 - acc: 0.6048
Epoch 4/5
8000/8000 [==============================] - 0s 39us/step - loss: 0.6545 - acc: 0.6134
Epoch 5/5
8000/8000 [==============================] - 0s 39us/step - loss: 0.6559 - acc: 0.6039
2000/2000 [==============================] - 0s 33us/step
Test accuracy: 0.592
Now, the question is, if I try to change the input size from 28x28 to, for example 128x128 the result is this:
Train images shape : (8000, 128, 128)
Labels images shape : (8000,)
Test images shape : (2000, 128, 128)
Test images shape : (2000,)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 16384) 0
_________________________________________________________________
dense (Dense) (None, 128) 2097280
_________________________________________________________________
dense_1 (Dense) (None, 2) 258
=================================================================
Total params: 2,097,538
Trainable params: 2,097,538
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/5
2018-07-27 15:27:41.966860: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
8000/8000 [==============================] - 4s 483us/step - loss: 8.0341 - acc: 0.4993
Epoch 2/5
8000/8000 [==============================] - 3s 362us/step - loss: 8.0590 - acc: 0.5000
Epoch 3/5
8000/8000 [==============================] - 3s 351us/step - loss: 8.0590 - acc: 0.5000
Epoch 4/5
8000/8000 [==============================] - 3s 342us/step - loss: 8.0590 - acc: 0.5000
Epoch 5/5
8000/8000 [==============================] - 3s 342us/step - loss: 8.0590 - acc: 0.5000
2000/2000 [==============================] - 0s 217us/step
Test accuracy: 0.5
Why? Though adding a new dense layer or increasing the neuron numbers the result is the same.
What is the connection between the input size and the model layers? Thanks!
