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signatue_recognition.py
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signatue_recognition.py
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import keras
from sklearn.model_selection import train_test_split
TEST_DIR='E:/Python/signatue_recognition/data/test/'
SIGNATURE_CLASSES = ['A', 'B', 'C','D','E','F','K','L','M','N','O','P']
import os, random
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
from matplotlib import ticker
#import seaborn as sns
#%matplotlib inline
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Convolution2D, MaxPooling2D, ZeroPadding2D, Dense, Activation
from keras.optimizers import SGD, Adagrad
from keras.callbacks import EarlyStopping
from keras.utils import np_utils
from keras.optimizers import RMSprop, Adam
from keras import backend as K
ROWS = 190
COLS = 160
CHANNELS = 3
TRAIN_DIR="E:/Python/signatue_recognition/data/train/"
def root_mean_squared_error(y_true, y_pred):
"""
RMSE loss function
"""
return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))
def get_images(fish):
"""Load files from train folder"""
fish_dir = TRAIN_DIR+'{}'.format(fish)
images = [fish+'/'+im for im in os.listdir(fish_dir)]
return images
def read_image(src):
import os
from scipy import misc
filepath=src
im=misc.imread(filepath)
import scipy.misc as mc
return mc.imresize(im,(ROWS,COLS))
files = []
y_all = []
for fish in SIGNATURE_CLASSES:
fish_files = get_images(fish)
files.extend(fish_files)
y_fish = np.tile(fish, len(fish_files))
y_all.extend(y_fish)
print("{0} photos of {1}".format(len(fish_files), fish))
y_all = np.array(y_all)
print(len(files))
print(len(y_all))
X_all = np.ndarray((len(files), ROWS, COLS, CHANNELS), dtype=np.uint8)
for i, im in enumerate(files):
X_all[i] = read_image(TRAIN_DIR+im)
if i%1000 == 0: print('Processed {} of {}'.format(i, len(files)))
print(X_all.shape)
y_all = LabelEncoder().fit_transform(y_all)
y_all = np_utils.to_categorical(y_all)
from sklearn.model_selection import train_test_split
X_train, X_valid, y_train, y_valid = train_test_split(X_all, y_all,
test_size=14, random_state=23,
stratify=y_all)
optimizer = RMSprop(lr=1e-4)
objective = 'categorical_crossentropy'
def center_normalize(x):
return (x - K.mean(x)) / K.std(x)
print('1')
model = Sequential()
model.add(Activation(activation=center_normalize, input_shape=(ROWS, COLS, CHANNELS)))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='valid'))
model.add(Activation('relu'))
model.add(ZeroPadding2D(padding=(1, 1)))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(96, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(96, 3, 3, border_mode='valid'))
model.add(Activation('relu'))
model.add(ZeroPadding2D(padding=(1, 1)))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(128, 2, 2, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(128, 2, 2, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(len(SIGNATURE_CLASSES)))
model.add(Activation('sigmoid'))
adam = Adam(lr=0.0001)
model.compile(optimizer=adam, loss=root_mean_squared_error)
early_stopping = EarlyStopping(monitor='val_loss', patience=4, verbose=1, mode='auto')
model.fit(X_train, y_train, batch_size=64, nb_epoch=3,
validation_split=0.1, verbose=1, shuffle=True, callbacks=[early_stopping])
preds = model.predict(X_valid, verbose=1)
print("Validation Log Loss: {}".format(log_loss(y_valid, preds)))
test_files = [im for im in os.listdir(TEST_DIR)]
test = np.ndarray((len(test_files), ROWS, COLS, CHANNELS), dtype=np.uint8)
for i, im in enumerate(test_files):
test[i] = read_image(TEST_DIR+im)
test_preds = model.predict(test, verbose=1)
submission = pd.DataFrame(test_preds, columns=SIGNATURE_CLASSES)
submission.insert(0, 'image', test_files)
submission.head()
submission.to_csv('E:/Python/signatue_recognition/signatureResults.csv',index=False)