import time
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Activation, BatchNormalization
from keras.regularizers import l2
from cnn.load_data_std import get_data
x_train, y_train = get_data(True, '../train.csv')
model = Sequential()
model.add(Conv2D(filters=36,
kernel_size=(3, 3),
padding='same',
input_shape=(28, 28, 1),
activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=36,
kernel_size=(3, 3),
padding='same',
activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=36,
kernel_size=(3, 3),
padding='same',
activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Conv2D(filters=36,
# kernel_size=(3, 3),
# padding='same',
# activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
from cnn.load_data_std import get_data
from keras.models import load_model
import matplotlib.pyplot as plt
import numpy as np
import itertools
from sklearn.metrics import confusion_matrix
X_validation, y_validation = get_data(False)
model = load_model('cnn_train2_model.h5')
def plot_confusion_matrix(cm,
classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
from cnn.load_data_std import get_data
from keras.models import load_model
x_test, y_test = get_data(False)
model = load_model('cnn_train2_model.h5')
# model = load_model('cnn_train_model.h5')
scores = model.evaluate(x_test, y_test)
print('CNN, test score: ', scores[1])
with open('cnn_predict_info.txt', 'w') as file:
file.write('test accuracy = {}\n'.format(scores[1]))
import time
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Activation, BatchNormalization
from keras.regularizers import l2
from cnn.load_data_std import get_data
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
import matplotlib.pyplot as plt
x_train, y_train = get_data(True)
print('x_train.shape: ', x_train.shape)
print('y_train.shape: ', y_train.shape)
model = Sequential()
model.add(Conv2D(filters=36,
kernel_size=(3, 3),
padding='same',
input_shape=(28, 28, 1),
activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=36,
kernel_size=(3, 3),
padding='same',
activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=36,
kernel_size=(3, 3),
padding='same',
activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=36,
kernel_size=(3, 3),
import time
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Activation, BatchNormalization
from keras.regularizers import l2
from cnn.load_data_std import get_data
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGenerator
x_train, y_train = get_data(True)
print('x_train.shape: ', x_train.shape)
print('y_train.shape: ', y_train.shape)
model = Sequential()
model.add(
Conv2D(filters=36,
kernel_size=(3, 3),
padding='same',
input_shape=(28, 28, 1),
activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(
Conv2D(filters=36, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(
Conv2D(filters=36, kernel_size=(3, 3), padding='same', activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(
Conv2D(filters=36, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
