Handwritten digit recognition from MNIST dataset. (#118)

examples/keras/digit_recognizer MNIST.py

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+# -*- coding: utf-8 -*-
+"""Digit-Recognizer( Kaggle).ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1LD2iIUkb58t1vYCTG-v8XLATn15FSTem
+
+Import necessary libraries
+"""
+
+import numpy as np
+import pandas as pd
+
+"""Clone the git repository to use the dataset"""
+
+!git clone https://github.com/mohitwildbeast/Digit-Recognizer-Kaggle.git
+
+!ls
+# %cd Digit-Recognizer-Kaggle
+
+train = pd.read_csv("data/train.csv")
+print(train.shape)
+
+test = pd.read_csv("data/test.csv")
+print(test.shape)
+
+x_train = train.iloc[:, 1:785].values
+y_train = train.iloc[:, 0].values
+x_test = test.iloc[:, 0:784].values
+
+print(x_train.shape)
+print(y_train.shape)
+print(x_test.shape)
+
+x_train = x_train/255.0
+x_test = x_test/255.0
+
+x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
+x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
+
+print(x_train.shape)
+print(x_test.shape)
+
+from keras.models import Sequential
+from keras.layers import Conv2D
+from keras.layers import MaxPooling2D
+from keras.layers import Dropout
+from keras.layers import Flatten
+from keras.layers import Dense
+from keras.utils import to_categorical
+from keras.utils import np_utils
+
+num_classes = 10
+batch_size = 64
+epochs = 40
+input_shape = (28,28,1)
+
+seed = 5
+np.random.seed(seed)
+
+y_train = to_categorical(y_train, num_classes)
+
+from sklearn.model_selection import train_test_split
+x_train, x_validation, y_train, y_validation = train_test_split(x_train, y_train, test_size = 0.2, random_state=seed)
+
+print(x_train.shape)
+print(x_validation.shape)
+print(y_train.shape)
+print(y_validation.shape)
+
+model = Sequential()
+model.add(Conv2D(32, kernel_size = (3,3), input_shape= input_shape, activation = 'relu'))
+model.add(MaxPooling2D(pool_size = (2,2)))
+model.add(Dropout(0.2))
+model.add(Conv2D(32, (3,3), activation = 'relu'))
+model.add(MaxPooling2D())
+model.add(Dropout(0.2))
+model.add(Flatten())
+model.add(Dense(512,activation = 'relu'))
+model.add(Dense(512, activation = 'relu'))
+model.add(Dense(num_classes, activation = 'sigmoid'))
+
+model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
+
+model.summary()
+
+model.fit(x_train, y_train, epochs = epochs, verbose = 1)
+
+loss , accuracy = model.evaluate(x_validation, y_validation, verbose = 0)
+print("Loss : ",loss, "Accuracy : ", accuracy)
+
+predicted_classes = model.predict_classes(x_test)
+
+submissions=pd.DataFrame({"ImageId": list(range(1,len(predicted_classes)+1)), "Label": predicted_classes})
+
+submissions.to_csv("submission.csv", index = False, header = True)
+
+from google.colab import files
+files.download('submission.csv')