Last active
April 21, 2021 03:43
-
-
Save mohdsanadzakirizvi/59f43521e48bb968a163bfad85237136 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # keras imports for the dataset and building our neural network | |
| from keras.datasets import mnist | |
| from keras.models import Sequential | |
| from keras.layers import Dense, Dropout, Conv2D, MaxPool2D | |
| from keras.utils import np_utils | |
| # Flattening the images from the 28x28 pixels to 1D 787 pixels | |
| X_train = X_train.reshape(60000, 784) | |
| X_test = X_test.reshape(10000, 784) | |
| X_train = X_train.astype('float32') | |
| X_test = X_test.astype('float32') | |
| # normalizing the data to help with the training | |
| X_train /= 255 | |
| X_test /= 255 | |
| # one-hot encoding using keras' numpy-related utilities | |
| n_classes = 10 | |
| print("Shape before one-hot encoding: ", y_train.shape) | |
| Y_train = np_utils.to_categorical(y_train, n_classes) | |
| Y_test = np_utils.to_categorical(y_test, n_classes) | |
| print("Shape after one-hot encoding: ", Y_train.shape) | |
| # building a linear stack of layers with the sequential model | |
| model = Sequential() | |
| # hidden layer | |
| model.add(Dense(100, input_shape=(784,), activation='relu')) | |
| # output layer | |
| model.add(Dense(10, activation='softmax')) | |
| # looking at the model summary | |
| model.summary() | |
| # compiling the sequential model | |
| model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam') | |
| # training the model for 10 epochs | |
| model.fit(X_train, Y_train, batch_size=128, epochs=10, validation_data=(X_test, Y_test)) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment