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Commit 738a729d authored by Tanja Vähämäki's avatar Tanja Vähämäki
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Vain koodi näkyvillä

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CatsOrButterflies.ipynb
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CatsOrDogs.ipynb
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CatsOrDogs1.ipynb
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%% Cell type:code id: tags:
``` python
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
```
%% Cell type:code id: tags:
``` python
DATADIR="C:/Users/Omistaja/Documents/kagglecatsanddogs_3367a/PetImages2"
```
%% Cell type:code id: tags:
``` python
CATEGORIES=["Dog","Cat"]
# CATEGORIES=["Dog","Cat","Butterfly"]
```
%% Cell type:code id: tags:
``` python
for category in CATEGORIES:
path=os.path.join(DATADIR,category)
for img in os.listdir(path):
img_array=cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE)
plt.imshow(img_array,cmap="gray")
plt.show()
break
break
```
%% Cell type:code id: tags:
``` python
print(img_array.shape)
```
%% Cell type:code id: tags:
``` python
IMG_SIZE=50
```
%% Cell type:code id: tags:
``` python
new_array=cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))
```
%% Cell type:code id: tags:
``` python
plt.imshow(new_array,cmap='gray')
```
%% Cell type:code id: tags:
``` python
plt.show()
```
%% Cell type:code id: tags:
``` python
training_data=[]
```
%% Cell type:code id: tags:
``` python
def create_training_data():
for category in CATEGORIES:
path=os.path.join(DATADIR,category)
class_num=CATEGORIES.index(category)
for img in os.listdir(path):
try:
img_array=cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE)
new_array=cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))
training_data.append([new_array,class_num])
except Exception as e:
pass
create_training_data()
```
%% Cell type:code id: tags:
``` python
print(len(training_data))
```
%% Cell type:code id: tags:
``` python
import random
random.shuffle(training_data)
```
%% Cell type:code id: tags:
``` python
for sample in training_data[:10]:
print(sample[1])
plt.imshow(sample[0], cmap='gray')
plt.show()
```
%% Cell type:code id: tags:
``` python
X=[]
y=[]
```
%% Cell type:code id: tags:
``` python
for features, label in training_data:
X.append(features)
y.append(label)
X=np.array(X).reshape(-1,IMG_SIZE,IMG_SIZE,1)
```
%% Cell type:code id: tags:
``` python
import pickle
pickle_out=open("X.pickle","wb")
pickle.dump(X,pickle_out)
pickle_out.close()
pickle_out=open("y.pickle","wb")
pickle.dump(y,pickle_out)
pickle_out.close()
```
%% Cell type:code id: tags:
``` python
pickle_in=open("X.pickle","rb")
X=pickle.load(pickle_in)
pickle_in=open("y.pickle","rb")
y=pickle.load(pickle_in)
```
%% Cell type:code id: tags:
``` python
X[1]
```
%% Cell type:code id: tags:
``` python
y[1]
```
%% Cell type:code id: tags:
``` python
import tensorflow as tf
```
%% Cell type:code id: tags:
``` python
from tensorflow.keras.models import Sequential
```
%% Cell type:code id: tags:
``` python
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
```
%% Cell type:code id: tags:
``` python
from tensorflow.keras.callbacks import TensorBoard # lisätty
import time # lisätty
```
%% Cell type:code id: tags:
``` python
from tensorflow.keras.datasets import cifar10 # lisätty
```
%% Cell type:code id: tags:
``` python
from tensorflow.keras.preprocessing.image import ImageDataGenerator # lisätty
```
%% Cell type:code id: tags:
``` python
gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.333) # lisätty
sess=tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) # lisätty
file_writer=tf.summary.FileWriter('C:/Users/Omistaja/Anaconda3/Scripts/logs/',sess.graph) # lisätty
```
%% Cell type:code id: tags:
``` python
X = X/255.0
```
%% Cell type:code id: tags:
``` python
dense_layers=[0]
```
%% Cell type:code id: tags:
``` python
layer_sizes=[64]
layer_sizes=[32]
```
%% Cell type:code id: tags:
``` python
conv_layers=[3]
conv_layers=[4]
```
%% Cell type:code id: tags:
``` python
for dense_layer in dense_layers:
for layer_size in layer_sizes:
for conv_layer in conv_layers:
NAME = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer,layer_size,dense_layer,int(time.time()))
print(NAME)
tensorboard=TensorBoard(log_dir='C:/Users/Omistaja/Anaconda3/Scripts/logs/{}'.format(NAME)) # lisätty
```
%% Cell type:code id: tags:
``` python
model=Sequential()
model.add(Conv2D(layer_size,(3,3),input_shape=X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
```
%% Cell type:code id: tags:
``` python
for l in range(conv_layer-1):
model.add(Conv2D(layer_size,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
```
%% Cell type:code id: tags:
``` python
model.add(Flatten())
for l in range(dense_layer):
model.add(Dense(layer_size))
model.add(Activation('relu'))
```
%% Cell type:code id: tags:
``` python
model.add(Dense(1))
model.add(Activation('sigmoid'))
```
%% Cell type:code id: tags:
``` python
model.compile(loss="binary_crossentropy",optimizer="adam",metrics=['accuracy'])
```
%% Cell type:code id: tags:
``` python
model.fit(X,y,batch_size=32, epochs=10, validation_split=0.1, callbacks=[tensorboard])
```
%% Cell type:code id: tags:
``` python
model.save('CatsOrDogsSmall64x3-CNN.model')
model.save('CatsOrDogsSmall32x4-CNN.model')
```
%% Cell type:code id: tags:
``` python
```
%% Cell type:code id: tags:
``` python
```
......
TestNewPhotos.ipynb
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