def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # Load the Digit DataSet load_data = LoadData() train_set_x, train_set_y = load_data.load_train_data("/home/darshan/Documents/DigitRecognizer/MNIST_data/", "train.csv") #mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) x = tf.placeholder(tf.float32, [None, 784]) x_image = tf.reshape(x, [-1, 28, 28, 1]) y_ = tf.placeholder(tf.float32, [None, 10]) # First Layer of Convnet W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) # 28 x 28 -> 24 x 24 h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # 24 x 24 -> 12 x 12 h_pool1 = max_pool_2x2(h_conv1)