import numpy as np import matplotlib.pyplot as plt from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense from keras.optimizers import SGD (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.reshape(x_train.shape[0], x_train.shape[1]*x_train.shape[1])/255.0 x_test = x_test.reshape(x_test.shape[0], x_test.shape[1]*x_test.shape[1])/255.0 #10 numbers 10 classes y_train = np_utils.to_categorical(y_train, num_classes=10) y_test = np_utils.to_categorical(y_test, num_classes=10) #result #Create model input 784 neurons output 10 neurons model = Sequential([ Dense(units=10,input_dim=784,bias_initializer='one',activation='softmax') ]) sgd = SGD(lr=0.2) model.compile( optimizer=sgd, loss='mse', metrics=['accuracy'] ) #epochs iteration period #Training model.fit(x_train,y_train,batch_size=32,epochs=10) #evaluate the model with the results loss, accuracy = model.evaluate(x_test,y_test) print('\ntest loss',loss) print('accuracy',accuracy)
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