base code

Author: paliwalabhishek

Diff for: CNN.py

@@ -0,0 +1,70 @@
+# save the final model to file
+from tensorflow.keras.datasets import fashion_mnist
+from keras.utils import to_categorical
+from keras.models import Sequential
+from keras.layers import Conv2D
+from keras.layers import MaxPooling2D
+from keras.layers import Dense
+from keras.layers import Flatten
+from keras.optimizers import SGD
+
+
+# load train and test dataset
+def load_dataset():
+    # load dataset
+    (trainX, trainY), (testX, testY) = fashion_mnist.load_data()
+    # reshape dataset to have a single channel
+    trainX = trainX.reshape((trainX.shape[0], 28, 28, 1))
+    testX = testX.reshape((testX.shape[0], 28, 28, 1))
+    # one hot encode target values
+    trainY = to_categorical(trainY)
+    testY = to_categorical(testY)
+    return trainX, trainY, testX, testY
+
+
+# scale pixels
+def prep_pixels(train, test):
+    # convert from integers to floats
+    train_norm = train.astype('float32')
+    test_norm = test.astype('float32')
+    # normalize to range 0-1
+    train_norm = train_norm / 255.0
+    test_norm = test_norm / 255.0
+    # return normalized images
+    return train_norm, test_norm
+
+
+# define cnn model
+def define_model():
+    model = Sequential()
+    model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(28, 28, 1)))
+    model.add(MaxPooling2D((2, 2)))
+    model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform'))
+    model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform'))
+    model.add(MaxPooling2D((2, 2)))
+    model.add(Flatten())
+    model.add(Dense(100, activation='relu', kernel_initializer='he_uniform'))
+    model.add(Dense(10, activation='softmax'))
+    # compile model
+    # opt = SGD(lr=0.01, momentum=0.9)
+    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+    return model
+
+
+# run the test harness for evaluating a model
+def run_test_harness():
+    # load dataset
+    trainX, trainY, testX, testY = load_dataset()
+    # prepare pixel data
+    trainX, testX = prep_pixels(trainX, testX)
+    # define model
+    model = define_model()
+    # fit model
+    model.fit(trainX, trainY, epochs=10, batch_size=100)
+    # evaluate model on test dataset
+    _, acc = model.evaluate(testX, testY)
+    print('> %.3f' % (acc * 100.0))
+
+
+# entry point, run the test harness
+run_test_harness()