1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | import numpy as npimport tensorflow as tffrom tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2Dimport randomrandom.seed(1)np.random.seed(1)tf.random.set_seed(1)(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()# checking unique labelsnp.unique(y_train)# reshaping to one channel colorX_train = X_train.reshape(X_train.shape[0], 28, 28, 1)X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)# normalizing X_train_normalized = X_train.astype('float32')/255.0X_test_normalized = X_test.astype('float32')/255.0#encoding label classesy_train_encoded = tf.keras.utils.to_categorical(y_train)y_test_encoded = tf.keras.utils.to_categorical(y_test)#building CNN modelmodel = Sequential( [ Conv2D(64, kernel_size=(3,3), input_shape=(28, 28, 1), activation='relu', padding='same'), MaxPooling2D(pool_size=(2, 2), padding='same'), Flatten(), Dense(100, activation='relu'), Dense(10, activation='softmax') ])# checking trainable parametersmodel.summary()#compiling modelmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])# fitting with 5 epochsmodel.fit(X_train_normalized, y_train_encoded, epochs=5)# checking training set accuracymodel.evaluate(X_train_normalized, y_train_encoded)# checking test set accuracymodel.evaluate(X_test_normalized, y_test_encoded) |
