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Kaggle_Handwritten_Digit_Recogniser.py
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Kaggle_Handwritten_Digit_Recogniser.py
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import pandas as pd
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D, LSTM
from keras.optimizers import SGD, Adam
from keras.regularizers import l2
np.random.seed(7)
learning_rate = 1e-4
train_iterations = 2000
dropout = 0.5
batch_size = 50
validation_size = 2000
image_to_display = 10
num_labels = 10
data_train = pd.read_table('Kagg/train.csv', delimiter = ',')
data_test = pd.read_table('Kagg/test.csv', delimiter = ',')
images_train = data_train.iloc[:,1:].values
images_test = data_test.values
images_train = images_train.astype(np.float)
images_test = images_test.astype(np.float)
images_train = np.multiply(images_train, 1.0/255.0)
images_test = np.multiply(images_test, 1.0/255.0)
train_labels_flat = data_train[[0]].values.ravel()
train_labels_count = np.unique(train_labels_flat).shape[0]
train_labels = to_categorical(np.asarray(train_labels_flat))
train_labels = train_labels.astype(np.uint8)
images_train = images_train.reshape(images_train.shape[0],28,28)
images_test = images_test.reshape(images_test.shape[0],28,28)
height = 28
width = 28
images_train = images_train.reshape(images_train.shape[0], 1, height, width).astype('float32')
images_test = images_test.reshape(images_test.shape[0], 1, height, width).astype('float32')
model = Sequential()
model.add(Convolution2D(16, 5, 5, border_mode='valid', input_shape=(1, 28, 28), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 3, 3, activation='relu',border_mode='valid'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 2, 2, activation='relu',border_mode='valid'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.25))
model.add(Dense(256, activation='relu'))
#model.add(Dropout(0.25))
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
# print(model.summary())
adam = Adam(lr = 0.01 , decay = 10**-4)
model.compile(loss = 'categorical_crossentropy', optimizer = adam,
metrics=['accuracy'])
model.fit(images_train, train_labels, batch_size = 128 , nb_epoch = 10)
model.save('generator_model.h5')
prediction = model.predict_classes(images_test)
import csv
with open("submit.csv","w", newline="") as out:
spamwriter = csv.writer(out, delimiter=',')
spamwriter.writerow(['ImageId','Label'])
for i in range(len(images_test)):
spamwriter.writerow([i+1,prediction[i]])