2D Convolution – Orbifold Consulting

    import numpy as np
    from matplotlib import pyplot as plt

    from keras.utils.np_utils import to_categorical
    from keras.models import Sequential
    from keras.layers.core import Dense, Dropout, Flatten
    from keras.layers import Conv2D
    from keras.callbacks import EarlyStopping
    from keras.datasets import mnist


    (X_train, y_train), (X_test, y_test) = mnist.load_data()

    img_rows, img_cols = X_train[0].shape[0], X_train[0].shape[1]
    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

    X_train = X_train.astype('float32')/255.
    X_test = X_test.astype('float32')/255.

    n_classes = len(set(y_train))
    y_train = to_categorical(y_train, n_classes)
    y_test = to_categorical(y_test, n_classes)

    model = Sequential()
    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
    model.add(Conv2D(256, kernel_size=(3, 3), activation='relu'))
    model.add(Dropout(0.5))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(n_classes, activation='softmax'))

    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

    callbacks = [EarlyStopping(monitor='val_acc', patience=5)]

    batch_size = 128
    n_epochs = 200

    model.fit(X_train, y_train, batch_size=batch_size, epochs=n_epochs, verbose=1, validation_split=0.2, callbacks=callbacks)

    score = model.evaluate(X_test, y_test, verbose=0)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])

    # Extract predictions
    preds = model.predict(X_test)

    n_examples = 10
    plt.figure(figsize=(15, 15))
    for i in range(n_examples):
        ax = plt.subplot(2, n_examples, i + 1)
        plt.imshow(X_test[i, :, :, 0], cmap='gray')
        plt.title("Label: {}\nPredicted: {}".format(np.argmax(y_test[i]), np.argmax(preds[i])))
        plt.axis('off')

    plt.show()

    plt.figure(figsize=(15, 15))

    j=1
    for i in range(len(y_test)):
        if(j&gt;10):
            break
        label = np.argmax(y_test[i])
        pred = np.argmax(preds[i])
        if label != pred:        
            ax = plt.subplot(2, n_examples, j)
            plt.imshow(X_test[i, :, :, 0], cmap='gray')
            plt.title("Label: {}\nPredicted: {}".format(label, pred))
            plt.axis('off')
            j+=1
    plt.show()

Using TensorFlow backend.

import numpy as np

from matplotlib import pyplot as plt

from keras.utils.np_utils import to_categorical

from keras.models import Sequential

from keras.layers.core import Dense, Dropout, Flatten

from keras.layers import Conv2D

from keras.callbacks import EarlyStopping

from keras.datasets import mnist

(X_train, y_train), (X_test, y_test) = mnist.load_data()

img_rows, img_cols = X_train[0].shape[0], X_train[0].shape[1]

X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)

X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)

input_shape = (img_rows, img_cols, 1)

X_train = X_train.astype('float32')/255.

X_test = X_test.astype('float32')/255.

n_classes = len(set(y_train))

y_train = to_categorical(y_train, n_classes)

y_test = to_categorical(y_test, n_classes)

model = Sequential()

model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=input_shape))

model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))

model.add(Conv2D(256, kernel_size=(3, 3), activation='relu'))

model.add(Dropout(0.5))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(n_classes, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

callbacks = [EarlyStopping(monitor='val_acc', patience=5)]

batch_size = 128

n_epochs = 200

model.fit(X_train, y_train, batch_size=batch_size, epochs=n_epochs, verbose=1, validation_split=0.2, callbacks=callbacks)

score = model.evaluate(X_test, y_test, verbose=0)

print('Test loss:', score[0])

print('Test accuracy:', score[1])

# Extract predictions

preds = model.predict(X_test)

n_examples = 10

plt.figure(figsize=(15, 15))

for i in range(n_examples):

ax = plt.subplot(2, n_examples, i + 1)

plt.imshow(X_test[i, :, :, 0], cmap='gray')

plt.title("Label: {}\nPredicted: {}".format(np.argmax(y_test[i]), np.argmax(preds[i])))

plt.axis('off')

plt.show()

plt.figure(figsize=(15, 15))

j=1

for i in range(len(y_test)):

if(j>10):

break

label = np.argmax(y_test[i])

pred = np.argmax(preds[i])

if label != pred:

ax = plt.subplot(2, n_examples, j)

plt.imshow(X_test[i, :, :, 0], cmap='gray')

plt.title("Label: {}\nPredicted: {}".format(label, pred))

plt.axis('off')

j+=1

plt.show()

Using TensorFlow backend.

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