def multilayer_perceptron(x, weights, biases): # Hidden layer with RELU activation layer_1 = tf.matmul(x, weights['h1']) + biases['b1'] layer_1 = tf.nn.relu(layer_1) # Hidden layer with RELU activation layer_2 = tf.matmul(layer_1, weights['h2']) + biases['b2'] layer_2 = tf.nn.relu(layer_2) # Output layer with linear activation out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] return out_layer
""" import your model here """ import your_model as tf """ your model should support the following code """ # create model x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x, W) + b) # define loss and optimizer y_ = tf.placeholder(tf.float32, [None, 10]) cross_entropy = tf.reduce_mean( -tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) W_grad = tf.gradients(cross_entropy, [W])[0] train_step = tf.assign(W, W - 0.5 * W_grad) sess = tf.Session() sess.run(tf.global_variables_initializer()) # get the mnist dataset (use tensorflow here) from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # train for _ in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) # second layer W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool1 = max_pool_2x2(h_conv2) # densely connected layer W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool1, [-1, 7 * 7 * 64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) # readout layer W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv = tf.matmul(h_fc1, W_fc2) + b_fc2 # loss cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) train_step = tf.train.GradientDescentOptimizer(1e-2).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
""" import your model here """ import your_model as tf """ your model should support the following code """ # create model x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x / 100.0, W) + b) # define loss and optimizer y_ = tf.placeholder(tf.float32, [None, 10]) cross_entropy = tf.reduce_mean( -tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) train_step = tf.train.AdamOptimizer(0.005).minimize(cross_entropy) sess = tf.Session() sess.run(tf.global_variables_initializer()) # get the mnist dataset (use tensorflow here) from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("FMNIST/", one_hot=True) # train for _ in range(10000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) # eval correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))