How to Upload Images for Image Classification in Jupyter
Image Classification
In this projection, nosotros'll classify images from the CIFAR-x dataset.
The dataset consists of airplanes, dogs, cats, and other objects.
Nosotros'll preprocess the images, then railroad train a convolutional neural network on all the samples.
The images need to exist normalized and the labels demand to be one-hot encoded.
We'll build a convolutional, max pooling, dropout, and fully connected layers.
At the end, we'll get to come across the neural network'south predictions on the sample images.
Get the Data
Run the following cell to download the CIFAR-ten dataset for python.
""" DON'T Modify ANYTHING IN THIS Prison cell THAT IS Beneath THIS LINE """ from urllib.asking import urlretrieve from os.path import isfile, isdir from tqdm import tqdm import problem_unittests as tests import tarfile cifar10_dataset_folder_path = 'cifar-10-batches-py' # Utilise Floyd'due south cifar-10 dataset if present floyd_cifar10_location = '/input/cifar-10/python.tar.gz' if isfile(floyd_cifar10_location): tar_gz_path = floyd_cifar10_location else: tar_gz_path = 'cifar-10-python.tar.gz' course DLProgress(tqdm): last_block = 0 def hook(self, block_num = 1, block_size = i, total_size = None): self.total = total_size self.update((block_num - self.last_block) * block_size) self.last_block = block_num if not isfile(tar_gz_path): with DLProgress(unit = 'B', unit_scale = True, miniters = i, desc = 'CIFAR-ten Dataset') as pbar: urlretrieve( 'https://world wide web.cs.toronto.edu/~kriz/cifar-10-python.tar.gz', tar_gz_path, pbar.hook) if non isdir(cifar10_dataset_folder_path): with tarfile.open up(tar_gz_path) every bit tar: tar.extractall() tar.close() tests.test_folder_path(cifar10_dataset_folder_path) Explore the Data
The dataset is broken into batches to prevent a computer from running out of memory.
The CIFAR-ten dataset consists of 5 batches, named data_batch_1, data_batch_2, etc.
Each batch contains the labels and images that are i of the post-obit:
- airplane
- automobile
- bird
- cat
- deer
- dog
- frog
- horse
- ship
- truck
Agreement a dataset is part of making predictions on the data.
Playing around with the code cell below by changing the batch_id and sample_id. The batch_id is the id for a batch (1-5).
The sample_id is the id for a image and label pair in the batch.
Good questions to ask are "What are all possible labels?", "What is the range of values for the image information?", "Are the labels in order or random?".
Answers to questions like these will help to preprocess the data and terminate up with better predictions.
% matplotlib inline % config InlineBackend.figure_format = 'retina' import helper import numpy equally np import sklearn # Explore the dataset batch_id = i sample_id = v helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id) Stats of batch 1: Samples: 10000 Label Counts: {0: 1005, ane: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, half dozen: 1030, seven: 1001, viii: 1025, 9: 981} Beginning 20 Labels: [half-dozen, 9, 9, 4, i, ane, 2, seven, 8, 3, 4, 7, seven, 2, 9, 9, 9, 3, two, 6] Case of Image five: Paradigm - Min Value: 0 Max Value: 252 Image - Shape: (32, 32, 3) Label - Characterization Id: one Name: auto 
Preprocess Functions
Normalize
The normalize function takes in image data, x, and returns information technology as a normalized Numpy array.
The values are in the range of 0 to one, inclusive.
The return object is of the same shape every bit x.
def normalize(10): """ Normalize a list of sample paradigm data in the range of 0 to 1 : ten: Listing of image information. The image shape is (32, 32, 3) : render: Numpy assortment of normalize information """ render np.array((10 - np.min(x)) / (np.max(x) - np.min(ten))) """ DON'T MODIFY ANYTHING IN THIS Prison cell THAT IS BELOW THIS LINE """ tests.test_normalize(normalize) Ane-hot encode
Another function for preprocessing.
This time the one_hot_encode function.
The input, ten, is a list of labels.
The office to returns the list of labels as Ane-Hot encoded Numpy array.
The possible values for labels are 0 to 9.
The one-hot encoding function returns the same encoding for each value between each call to one_hot_encode.
The map of encodings is saved exterior the role.
lb_encoding = None def one_hot_encode(x): """ One hot encode a listing of sample labels. Return a one-hot encoded vector for each label. : x: List of sample Labels : return: Numpy array of ane-hot encoded labels """ global lb_encoding if lb_encoding is not None: return lb_encoding.transform(x) else: labels = np.array(x) lb_encoding = sklearn.preprocessing.LabelBinarizer() lb_encoding.fit(labels) render lb_encoding.transform(labels) """ DON'T MODIFY ANYTHING IN THIS Prison cell THAT IS Beneath THIS LINE """ tests.test_one_hot_encode(one_hot_encode) Randomize Data
As we saw from exploring the data in a higher place, the lodge of the samples are randomized. It doesn't hurt to randomize it over again, but we don't need to for this dataset.
Preprocess all the data and save it
Running the lawmaking cell below volition preprocess all the CIFAR-ten information and save it to file. The code below also uses x% of the training data for validation.
""" DON'T Alter ANYTHING IN THIS CELL """ # Preprocess Training, Validation, and Testing Data helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode) Check Bespeak
This is the first checkpoint. If we ever decide to come back to this notebook or have to restart the notebook, we can start from hither. The preprocessed data has been saved to disk.
""" DON'T Alter Anything IN THIS Jail cell """ import pickle import problem_unittests as tests import helper # Load the Preprocessed Validation data valid_features, valid_labels = pickle.load(open up('preprocess_validation.p', mode = 'rb')) Build the network
For the neural network, you'll build each layer into a part. Almost of the code you've seen has been outside of functions. To test your code more thoroughly, nosotros require that you lot put each layer in a role. This allows us to give you improve feedback and test for simple mistakes using our unittests earlier you lot submit your project.
Notation: If you're finding it hard to dedicate plenty time for this course each week, we've provided a small shortcut to this function of the projection. In the next couple of problems, y'all'll have the option to apply classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages to build each layer, except the layers you build in the "Convolutional and Max Pooling Layer" department. TF Layers is like to Keras'south and TFLearn's abstraction to layers, so information technology'due south like shooting fish in a barrel to pickup.
Withal, if you would like to get the most out of this form, try to solve all the problems without using anything from the TF Layers packages. You tin still use classes from other packages that happen to take the same proper noun as ones you notice in TF Layers! For case, instead of using the TF Layers version of the
conv2dclass, tf.layers.conv2d, you lot would desire to apply the TF Neural Network version ofconv2d, tf.nn.conv2d.
Let's begin!
Input
The neural network needs to read the epitome data, ane-hot encoded labels, and dropout go along probability. Implement the post-obit functions
- Implement
neural_net_image_input - Return a TF Placeholder
- Fix the shape using
image_shapewith batch size fix toNone. - Name the TensorFlow placeholder "x" using the TensorFlow
nameparameter in the TF Placeholder. - Implement
neural_net_label_input - Return a TF Placeholder
- Gear up the shape using
n_classeswith batch size set toNone. - Name the TensorFlow placeholder "y" using the TensorFlow
nameparameter in the TF Placeholder. - Implement
neural_net_keep_prob_input - Render a TF Placeholder for dropout keep probability.
- Proper noun the TensorFlow placeholder "keep_prob" using the TensorFlow
proper nameparameter in the TF Placeholder.
These names will be used at the terminate of the projection to load your saved model.
Annotation: None for shapes in TensorFlow permit for a dynamic size.
import tensorflow as tf def neural_net_image_input(image_shape): """ Return a Tensor for a batch of image input : image_shape: Shape of the images : return: Tensor for image input. """ return tf.placeholder(tf.float32, shape =[None, image_shape[0], image_shape[1], image_shape[ii]], name = 'x') def neural_net_label_input(n_classes): """ Return a Tensor for a batch of label input : n_classes: Number of classes : render: Tensor for label input. """ return tf.placeholder(tf.float32, shape =[None, n_classes], name = 'y') def neural_net_keep_prob_input(): """ Render a Tensor for go along probability : return: Tensor for keep probability. """ return tf.placeholder(tf.float32, None, proper noun = 'keep_prob') """ DON'T MODIFY Anything IN THIS Jail cell THAT IS BELOW THIS LINE """ tf.reset_default_graph() tests.test_nn_image_inputs(neural_net_image_input) tests.test_nn_label_inputs(neural_net_label_input) tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input) Image Input Tests Passed. Label Input Tests Passed. Proceed Prob Tests Passed. Convolution and Max Pooling Layer
Convolution layers accept a lot of success with images. For this lawmaking cell, y'all should implement the function conv2d_maxpool to use convolution then max pooling:
- Create the weight and bias using
conv_ksize,conv_num_outputsand the shape ofx_tensor. - Utilise a convolution to
x_tensorusing weight andconv_strides. - We recommend you employ same padding, simply you're welcome to use any padding.
- Add bias
- Add a nonlinear activation to the convolution.
- Use Max Pooling using
pool_ksizeandpool_strides. - We recommend you apply same padding, but yous're welcome to use any padding.
Annotation: You can't use TensorFlow Layers or TensorFlow Layers (contrib) for this layer, but you can nonetheless utilise TensorFlow's Neural Network package. You lot may withal apply the shortcut option for all the other layers.
def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides): """ Apply convolution then max pooling to x_tensor :param x_tensor: TensorFlow Tensor :param conv_num_outputs: Number of outputs for the convolutional layer :param conv_ksize: kernal size ii-D Tuple for the convolutional layer :param conv_strides: Stride 2-D Tuple for convolution :param pool_ksize: kernal size ii-D Tuple for puddle :param pool_strides: Pace 2-D Tuple for puddle : return: A tensor that represents convolution and max pooling of x_tensor """ weights = tf.Variable(tf.truncated_normal([conv_ksize[0],conv_ksize[1], x_tensor.get_shape().as_list()[3], conv_num_outputs])) bias = tf.Variable(tf.zeros(conv_num_outputs)) convolution = tf.nn.conv2d(x_tensor, weights, strides =[1,conv_strides[0],conv_strides[one],one], padding = 'Same') convolution_and_bias = tf.nn.bias_add(convolution, bias) convolution_and_bias_activation = tf.nn.relu(convolution_and_bias) max_pool = tf.nn.max_pool(convolution_and_bias_activation, [i,pool_ksize[0],pool_ksize[1],i], strides =[ane,pool_strides[0],pool_strides[1],1], padding = 'SAME') return max_pool """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_con_pool(conv2d_maxpool) Flatten Layer
Implement the flatten function to modify the dimension of x_tensor from a 4-D tensor to a ii-D tensor. The output should exist the shape (Batch Size, Flattened Image Size). Shortcut option: yous can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
def flatten(x_tensor): """ Flatten x_tensor to (Batch Size, Flattened Image Size) : x_tensor: A tensor of size (Batch Size, ...), where ... are the paradigm dimensions. : return: A tensor of size (Batch Size, Flattened Image Size). """ render tf.contrib.layers.flatten(x_tensor) """ DON'T MODIFY Annihilation IN THIS Jail cell THAT IS Beneath THIS LINE """ tests.test_flatten(flatten) Fully-Connected Layer
Implement the fully_conn role to apply a fully continued layer to x_tensor with the shape (Batch Size, num_outputs). Shortcut pick: you tin utilize classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a claiming, only use other TensorFlow packages.
def fully_conn(x_tensor, num_outputs): """ Utilize a fully connected layer to x_tensor using weight and bias : x_tensor: A 2-D tensor where the commencement dimension is batch size. : num_outputs: The number of output that the new tensor should be. : render: A 2-D tensor where the 2d dimension is num_outputs. """ return tf.contrib.layers.fully_connected(x_tensor, num_outputs, activation_fn = tf.nn.relu, biases_initializer = tf.zeros_initializer(), trainable = True) """ DON'T Change ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_fully_conn(fully_conn) Output Layer
Implement the output function to apply a fully continued layer to x_tensor with the shape (Batch Size, num_outputs). Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
Note: Activation, softmax, or cross entropy should non exist applied to this.
def output(x_tensor, num_outputs): """ Use a output layer to x_tensor using weight and bias : x_tensor: A two-D tensor where the offset dimension is batch size. : num_outputs: The number of output that the new tensor should be. : return: A ii-D tensor where the second dimension is num_outputs. """ dimension = x_tensor.get_shape().as_list() shape = list( (dimension[- 1],) + (num_outputs,)) weight = tf.Variable(tf.truncated_normal(shape,0,0.01)) bias = tf.Variable(tf.zeros(num_outputs)) return tf.add(tf.matmul(x_tensor,weight), bias) # Implementation only using TensorFlow is commented below #return tf.contrib.layers.fully_connected(x_tensor, num_outputs, activation_fn=None) """ DON'T MODIFY Annihilation IN THIS CELL THAT IS Beneath THIS LINE """ tests.test_output(output) Create Convolutional Model
Implement the function conv_net to create a convolutional neural network model. The function takes in a batch of images, x, and outputs logits. Utilise the layers y'all created above to create this model:
- Apply 1, 2, or 3 Convolution and Max Puddle layers
- Employ a Flatten Layer
- Apply 1, 2, or 3 Fully Connected Layers
- Apply an Output Layer
- Render the output
- Apply TensorFlow'southward Dropout to one or more layers in the model using
keep_prob.
def conv_net(x, keep_prob): """ Create a convolutional neural network model : x: Placeholder tensor that holds image data. : keep_prob: Placeholder tensor that concur dropout continue probability. : return: Tensor that represents logits """ layer = conv2d_maxpool(x, 64, (four,4), (1,1), (two,2), (2,two)) tf.nn.dropout(layer, keep_prob = keep_prob) # Apply a Flatten Layer layer = flatten(layer) # Apply 2 Fully Connected Layers layer = fully_conn(layer,500) layer = tf.nn.dropout(layer, keep_prob) layer = fully_conn(layer,100) layer = tf.nn.dropout(layer, keep_prob) # Render output render output(layer,10) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS Below THIS LINE """ ############################## ## Build the Neural Network ## ############################## # Remove previous weights, bias, inputs, etc.. tf.reset_default_graph() # Inputs ten = neural_net_image_input((32, 32, three)) y = neural_net_label_input(10) keep_prob = neural_net_keep_prob_input() # Model logits = conv_net(x, keep_prob) # Proper name logits Tensor, and then that is can exist loaded from disk after training logits = tf.identity(logits, name = 'logits') # Loss and Optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = y)) optimizer = tf.train.AdamOptimizer().minimize(price) # Accuracy correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, one)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), proper noun = 'accuracy') tests.test_conv_net(conv_net) Railroad train the Neural Network
Unmarried Optimization
Implement the function train_neural_network to do a single optimization. The optimization should use optimizer to optimize in session with a feed_dict of the following:
-
xfor image input -
yfor labels -
keep_probfor proceed probability for dropout
This function will exist called for each batch, so tf.global_variables_initializer() has already been called.
Note: Nada needs to be returned. This part is but optimizing the neural network.
def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch): """ Optimize the session on a batch of images and labels : session: Current TensorFlow session : optimizer: TensorFlow optimizer office : keep_probability: keep probability : feature_batch: Batch of Numpy image data : label_batch: Batch of Numpy label information """ # TODO: Implement Office session.run(optimizer, feed_dict ={ten:feature_batch, y:label_batch, keep_prob:keep_probability}) """ DON'T Modify Annihilation IN THIS Cell THAT IS BELOW THIS LINE """ tests.test_train_nn(train_neural_network) Show Stats
Implement the function print_stats to print loss and validation accuracy. Use the global variables valid_features and valid_labels to calculate validation accuracy. Employ a go along probability of 1.0 to calculate the loss and validation accuracy.
def print_stats(session, feature_batch, label_batch, toll, accurateness): """ Impress information virtually loss and validation accuracy : session: Current TensorFlow session : feature_batch: Batch of Numpy paradigm data : label_batch: Batch of Numpy label data : cost: TensorFlow toll role : accurateness: TensorFlow accuracy function """ loss = session.run(cost, feed_dict ={x: feature_batch, y:label_batch, keep_prob:1.}) valid_accuracy = session.run(accuracy, feed_dict ={x: valid_features, y: valid_labels, keep_prob: 1.}) impress('Loss: {:>10.4f}'.format(loss)) print('Accuracy: {:.6f}'.format(valid_accuracy)) Hyperparameters
Tune the following parameters:
- Set
epochsto the number of iterations until the network stops learning or start overfitting - Gear up
batch_sizeto the highest number that your car has retention for. Most people fix them to common sizes of retentivity: - 64
- 128
- 256
- ...
- Set
keep_probabilityto the probability of keeping a node using dropout
# TODO: Tune Parameters epochs = 100 batch_size = 512 keep_probability = 0.five Railroad train on a Unmarried CIFAR-10 Batch
Instead of training the neural network on all the CIFAR-10 batches of data, allow'due south use a unmarried batch. This should save time while you lot iterate on the model to get a better accuracy. Once the concluding validation accuracy is l% or greater, run the model on all the information in the adjacent section.
""" DON'T Modify Anything IN THIS Prison cell """ impress('Checking the Training on a Single Batch...') with tf.Session() equally sess: # Initializing the variables sess.run(tf.global_variables_initializer()) # Preparation bicycle for epoch in range(epochs): batch_i = i for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size): train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels) print('Epoch {:>ii}, CIFAR-ten Batch {}: '.format(epoch + i, batch_i), end = '') print_stats(sess, batch_features, batch_labels, price, accuracy) Checking the Training on a Unmarried Batch... Epoch 1, CIFAR-10 Batch 1: Loss: ii.2741 Accuracy: 0.159200 Epoch 2, CIFAR-10 Batch one: Loss: 2.0728 Accurateness: 0.240600 Epoch three, CIFAR-10 Batch 1: Loss: ane.9645 Accurateness: 0.285800 Epoch 4, CIFAR-x Batch 1: Loss: one.8730 Accuracy: 0.336400 Epoch 5, CIFAR-ten Batch 1: Loss: 1.7617 Accurateness: 0.372600 Epoch six, CIFAR-10 Batch 1: Loss: 1.7057 Accuracy: 0.392000 Epoch 7, CIFAR-10 Batch i: Loss: ane.6158 Accurateness: 0.392800 Epoch 8, CIFAR-10 Batch 1: Loss: 1.5579 Accuracy: 0.429800 Epoch ix, CIFAR-10 Batch 1: Loss: 1.4939 Accuracy: 0.424200 Epoch x, CIFAR-ten Batch 1: Loss: 1.4390 Accuracy: 0.444000 Epoch 11, CIFAR-ten Batch i: Loss: i.4059 Accuracy: 0.452400 Epoch 12, CIFAR-x Batch one: Loss: 1.3298 Accuracy: 0.464800 Epoch xiii, CIFAR-ten Batch 1: Loss: one.3332 Accuracy: 0.450200 Epoch fourteen, CIFAR-x Batch 1: Loss: 1.2317 Accurateness: 0.488400 Epoch fifteen, CIFAR-10 Batch 1: Loss: 1.1988 Accuracy: 0.500000 Epoch 16, CIFAR-ten Batch i: Loss: 1.1245 Accurateness: 0.490800 Epoch 17, CIFAR-10 Batch i: Loss: 1.0743 Accuracy: 0.502800 Epoch 18, CIFAR-ten Batch one: Loss: 1.0410 Accuracy: 0.503200 Epoch nineteen, CIFAR-10 Batch i: Loss: 0.9986 Accuracy: 0.516200 Epoch 20, CIFAR-x Batch 1: Loss: 0.9680 Accurateness: 0.502800 Epoch 21, CIFAR-10 Batch one: Loss: 0.9311 Accuracy: 0.517200 Epoch 22, CIFAR-10 Batch ane: Loss: 0.8723 Accuracy: 0.529200 Epoch 23, CIFAR-ten Batch 1: Loss: 0.8511 Accuracy: 0.526600 Epoch 24, CIFAR-ten Batch 1: Loss: 0.8240 Accuracy: 0.518200 Epoch 25, CIFAR-x Batch 1: Loss: 0.7855 Accuracy: 0.529200 Epoch 26, CIFAR-10 Batch one: Loss: 0.8013 Accuracy: 0.522600 Epoch 27, CIFAR-10 Batch 1: Loss: 0.7687 Accuracy: 0.520400 Epoch 28, CIFAR-ten Batch one: Loss: 0.7500 Accurateness: 0.518600 Epoch 29, CIFAR-10 Batch 1: Loss: 0.7193 Accuracy: 0.522800 Epoch 30, CIFAR-x Batch 1: Loss: 0.6725 Accuracy: 0.523400 Epoch 31, CIFAR-10 Batch 1: Loss: 0.6301 Accuracy: 0.526200 Epoch 32, CIFAR-ten Batch i: Loss: 0.5753 Accurateness: 0.541600 Epoch 33, CIFAR-x Batch i: Loss: 0.5785 Accuracy: 0.530400 Epoch 34, CIFAR-10 Batch 1: Loss: 0.5513 Accurateness: 0.534200 Epoch 35, CIFAR-10 Batch 1: Loss: 0.5124 Accuracy: 0.534000 Epoch 36, CIFAR-x Batch ane: Loss: 0.5439 Accurateness: 0.537200 Epoch 37, CIFAR-10 Batch one: Loss: 0.5174 Accuracy: 0.546000 Epoch 38, CIFAR-10 Batch 1: Loss: 0.4844 Accuracy: 0.537000 Epoch 39, CIFAR-10 Batch 1: Loss: 0.4504 Accuracy: 0.540200 Epoch 40, CIFAR-10 Batch 1: Loss: 0.4402 Accuracy: 0.530600 Epoch 41, CIFAR-10 Batch 1: Loss: 0.4242 Accuracy: 0.531000 Epoch 42, CIFAR-ten Batch 1: Loss: 0.4097 Accuracy: 0.541200 Epoch 43, CIFAR-ten Batch ane: Loss: 0.4430 Accuracy: 0.536200 Epoch 44, CIFAR-x Batch 1: Loss: 0.3798 Accuracy: 0.541600 Epoch 45, CIFAR-10 Batch one: Loss: 0.3413 Accuracy: 0.545800 Epoch 46, CIFAR-10 Batch 1: Loss: 0.3427 Accurateness: 0.544200 Epoch 47, CIFAR-10 Batch 1: Loss: 0.3457 Accuracy: 0.552000 Epoch 48, CIFAR-ten Batch 1: Loss: 0.3256 Accuracy: 0.539000 Epoch 49, CIFAR-10 Batch 1: Loss: 0.3233 Accuracy: 0.550600 Epoch 50, CIFAR-10 Batch 1: Loss: 0.2978 Accuracy: 0.550800 Epoch 51, CIFAR-ten Batch 1: Loss: 0.3245 Accurateness: 0.538800 Epoch 52, CIFAR-10 Batch one: Loss: 0.3052 Accurateness: 0.547800 Epoch 53, CIFAR-10 Batch 1: Loss: 0.2695 Accuracy: 0.549200 Epoch 54, CIFAR-10 Batch 1: Loss: 0.2550 Accuracy: 0.544600 Epoch 55, CIFAR-x Batch 1: Loss: 0.2730 Accuracy: 0.544600 Epoch 56, CIFAR-10 Batch 1: Loss: 0.2652 Accuracy: 0.552600 Epoch 57, CIFAR-10 Batch i: Loss: 0.2726 Accurateness: 0.539800 Epoch 58, CIFAR-ten Batch ane: Loss: 0.2360 Accuracy: 0.540200 Epoch 59, CIFAR-x Batch i: Loss: 0.2193 Accuracy: 0.552600 Epoch 60, CIFAR-x Batch ane: Loss: 0.2399 Accuracy: 0.554600 Epoch 61, CIFAR-10 Batch 1: Loss: 0.2418 Accuracy: 0.544600 Epoch 62, CIFAR-10 Batch ane: Loss: 0.2363 Accuracy: 0.543600 Epoch 63, CIFAR-ten Batch 1: Loss: 0.2043 Accuracy: 0.545600 Epoch 64, CIFAR-10 Batch 1: Loss: 0.2109 Accuracy: 0.538800 Epoch 65, CIFAR-x Batch one: Loss: 0.2069 Accuracy: 0.543400 Epoch 66, CIFAR-10 Batch one: Loss: 0.2025 Accurateness: 0.549200 Epoch 67, CIFAR-10 Batch 1: Loss: 0.1713 Accuracy: 0.550800 Epoch 68, CIFAR-10 Batch i: Loss: 0.1739 Accuracy: 0.554800 Epoch 69, CIFAR-10 Batch 1: Loss: 0.1604 Accuracy: 0.554400 Epoch 70, CIFAR-10 Batch 1: Loss: 0.1651 Accuracy: 0.559600 Epoch 71, CIFAR-ten Batch ane: Loss: 0.1621 Accuracy: 0.552400 Epoch 72, CIFAR-10 Batch 1: Loss: 0.1432 Accuracy: 0.558000 Epoch 73, CIFAR-10 Batch 1: Loss: 0.1350 Accurateness: 0.563400 Epoch 74, CIFAR-10 Batch 1: Loss: 0.1361 Accuracy: 0.550600 Epoch 75, CIFAR-x Batch 1: Loss: 0.1241 Accurateness: 0.558600 Epoch 76, CIFAR-10 Batch 1: Loss: 0.1239 Accuracy: 0.556800 Epoch 77, CIFAR-ten Batch 1: Loss: 0.1249 Accuracy: 0.549800 Epoch 78, CIFAR-10 Batch i: Loss: 0.1183 Accuracy: 0.555400 Epoch 79, CIFAR-ten Batch i: Loss: 0.1109 Accuracy: 0.554600 Epoch fourscore, CIFAR-10 Batch one: Loss: 0.0996 Accuracy: 0.546600 Epoch 81, CIFAR-10 Batch 1: Loss: 0.1082 Accurateness: 0.552200 Epoch 82, CIFAR-10 Batch i: Loss: 0.0896 Accuracy: 0.544600 Epoch 83, CIFAR-10 Batch 1: Loss: 0.0927 Accurateness: 0.558400 Epoch 84, CIFAR-10 Batch 1: Loss: 0.0891 Accuracy: 0.551800 Epoch 85, CIFAR-10 Batch one: Loss: 0.0856 Accuracy: 0.554000 Epoch 86, CIFAR-10 Batch 1: Loss: 0.0794 Accuracy: 0.559000 Epoch 87, CIFAR-10 Batch 1: Loss: 0.0770 Accuracy: 0.558600 Epoch 88, CIFAR-10 Batch 1: Loss: 0.0765 Accuracy: 0.561000 Epoch 89, CIFAR-10 Batch ane: Loss: 0.0801 Accuracy: 0.562400 Epoch 90, CIFAR-10 Batch 1: Loss: 0.0768 Accurateness: 0.556800 Epoch 91, CIFAR-ten Batch i: Loss: 0.0765 Accurateness: 0.561200 Epoch 92, CIFAR-10 Batch 1: Loss: 0.0587 Accuracy: 0.556200 Epoch 93, CIFAR-x Batch 1: Loss: 0.0767 Accurateness: 0.557800 Epoch 94, CIFAR-10 Batch 1: Loss: 0.0717 Accurateness: 0.556800 Epoch 95, CIFAR-10 Batch 1: Loss: 0.0728 Accuracy: 0.560400 Epoch 96, CIFAR-10 Batch 1: Loss: 0.0674 Accuracy: 0.562800 Epoch 97, CIFAR-10 Batch one: Loss: 0.0756 Accuracy: 0.555800 Epoch 98, CIFAR-10 Batch 1: Loss: 0.0717 Accuracy: 0.557600 Epoch 99, CIFAR-10 Batch 1: Loss: 0.0767 Accuracy: 0.563800 Epoch 100, CIFAR-10 Batch one: Loss: 0.0674 Accuracy: 0.554400 Fully Railroad train the Model
Now that you got a good accuracy with a single CIFAR-x batch, endeavour information technology with all five batches.
""" DON'T Alter Anything IN THIS Prison cell """ save_model_path = './image_classification' print('Preparation...') with tf.Session() as sess: # Initializing the variables sess.run(tf.global_variables_initializer()) # Grooming bicycle for epoch in range(epochs): # Loop over all batches n_batches = v for batch_i in range(1, n_batches + one): for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size): train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels) print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), terminate = '') print_stats(sess, batch_features, batch_labels, cost, accuracy) # Save Model saver = tf.train.Saver() save_path = saver.save(sess, save_model_path) Training... Epoch 1, CIFAR-10 Batch ane: Loss: two.2740 Accuracy: 0.191400 Epoch one, CIFAR-10 Batch ii: Loss: 2.0493 Accuracy: 0.257600 Epoch 1, CIFAR-10 Batch 3: Loss: 1.8533 Accuracy: 0.305800 Epoch 1, CIFAR-10 Batch 4: Loss: one.7334 Accuracy: 0.370800 Epoch 1, CIFAR-ten Batch 5: Loss: i.7248 Accuracy: 0.401000 Epoch 2, CIFAR-ten Batch 1: Loss: 1.7207 Accuracy: 0.393600 Epoch 2, CIFAR-10 Batch 2: Loss: ane.5546 Accurateness: 0.419000 Epoch 2, CIFAR-10 Batch 3: Loss: 1.4243 Accuracy: 0.430400 Epoch 2, CIFAR-10 Batch four: Loss: one.4280 Accuracy: 0.443000 Epoch 2, CIFAR-x Batch five: Loss: one.4505 Accuracy: 0.467400 Epoch 3, CIFAR-10 Batch 1: Loss: 1.5237 Accuracy: 0.466400 Epoch 3, CIFAR-10 Batch 2: Loss: 1.3811 Accuracy: 0.473600 Epoch three, CIFAR-10 Batch 3: Loss: ane.2799 Accuracy: 0.467000 Epoch 3, CIFAR-10 Batch 4: Loss: 1.2639 Accuracy: 0.485400 Epoch 3, CIFAR-10 Batch five: Loss: ane.3058 Accuracy: 0.498800 Epoch four, CIFAR-10 Batch 1: Loss: 1.4044 Accurateness: 0.494200 Epoch 4, CIFAR-ten Batch ii: Loss: one.2766 Accuracy: 0.507200 Epoch 4, CIFAR-10 Batch iii: Loss: 1.1631 Accuracy: 0.504800 Epoch 4, CIFAR-ten Batch 4: Loss: ane.1875 Accuracy: 0.513600 Epoch iv, CIFAR-10 Batch v: Loss: 1.1926 Accuracy: 0.532800 Epoch 5, CIFAR-10 Batch 1: Loss: 1.2536 Accuracy: 0.539800 Epoch five, CIFAR-10 Batch 2: Loss: 1.1533 Accuracy: 0.538600 Epoch 5, CIFAR-ten Batch 3: Loss: 1.0515 Accurateness: 0.539200 Epoch 5, CIFAR-x Batch 4: Loss: 1.0981 Accuracy: 0.548000 Epoch five, CIFAR-x Batch 5: Loss: i.0772 Accurateness: 0.556400 Epoch 6, CIFAR-10 Batch 1: Loss: ane.1876 Accuracy: 0.548800 Epoch 6, CIFAR-ten Batch 2: Loss: 1.0804 Accuracy: 0.561200 Epoch half-dozen, CIFAR-10 Batch 3: Loss: 0.9763 Accuracy: 0.565600 Epoch 6, CIFAR-x Batch four: Loss: i.0355 Accurateness: 0.568600 Epoch 6, CIFAR-10 Batch 5: Loss: 0.9942 Accuracy: 0.566800 Epoch 7, CIFAR-x Batch 1: Loss: 1.0854 Accuracy: 0.572800 Epoch 7, CIFAR-10 Batch 2: Loss: one.0064 Accuracy: 0.576600 Epoch 7, CIFAR-10 Batch 3: Loss: 0.9067 Accuracy: 0.569600 Epoch 7, CIFAR-10 Batch four: Loss: 0.9718 Accuracy: 0.580000 Epoch 7, CIFAR-10 Batch v: Loss: 0.9460 Accuracy: 0.580800 Epoch 8, CIFAR-10 Batch i: Loss: i.0132 Accurateness: 0.586400 Epoch 8, CIFAR-10 Batch 2: Loss: 0.9411 Accuracy: 0.585600 Epoch viii, CIFAR-ten Batch 3: Loss: 0.8423 Accuracy: 0.583600 Epoch eight, CIFAR-x Batch 4: Loss: 0.9069 Accuracy: 0.581000 Epoch eight, CIFAR-10 Batch 5: Loss: 0.8663 Accuracy: 0.588400 Epoch 9, CIFAR-10 Batch ane: Loss: 0.9759 Accuracy: 0.584800 Epoch ix, CIFAR-x Batch ii: Loss: 0.8943 Accuracy: 0.587800 Epoch 9, CIFAR-10 Batch 3: Loss: 0.8552 Accuracy: 0.585200 Epoch 9, CIFAR-10 Batch 4: Loss: 0.8568 Accuracy: 0.580600 Epoch 9, CIFAR-ten Batch 5: Loss: 0.8175 Accuracy: 0.603400 Epoch 10, CIFAR-x Batch ane: Loss: 0.9361 Accuracy: 0.591000 Epoch ten, CIFAR-10 Batch two: Loss: 0.8515 Accuracy: 0.597200 Epoch 10, CIFAR-10 Batch 3: Loss: 0.8041 Accuracy: 0.595400 Epoch 10, CIFAR-10 Batch 4: Loss: 0.8099 Accuracy: 0.599000 Epoch 10, CIFAR-ten Batch five: Loss: 0.7897 Accuracy: 0.605400 Epoch 11, CIFAR-ten Batch 1: Loss: 0.8804 Accuracy: 0.601800 Epoch 11, CIFAR-10 Batch 2: Loss: 0.7989 Accurateness: 0.604000 Epoch 11, CIFAR-ten Batch 3: Loss: 0.7587 Accuracy: 0.596200 Epoch eleven, CIFAR-x Batch 4: Loss: 0.7657 Accuracy: 0.605200 Epoch eleven, CIFAR-x Batch 5: Loss: 0.7342 Accuracy: 0.606200 Epoch 12, CIFAR-10 Batch 1: Loss: 0.8349 Accurateness: 0.609200 Epoch 12, CIFAR-x Batch 2: Loss: 0.7855 Accuracy: 0.605200 Epoch 12, CIFAR-10 Batch 3: Loss: 0.7085 Accurateness: 0.612600 Epoch 12, CIFAR-ten Batch 4: Loss: 0.7351 Accuracy: 0.612400 Epoch 12, CIFAR-10 Batch five: Loss: 0.7091 Accurateness: 0.605400 Epoch 13, CIFAR-10 Batch ane: Loss: 0.8235 Accurateness: 0.606200 Epoch 13, CIFAR-ten Batch 2: Loss: 0.7455 Accuracy: 0.612200 Epoch 13, CIFAR-ten Batch 3: Loss: 0.6876 Accurateness: 0.609600 Epoch xiii, CIFAR-10 Batch 4: Loss: 0.7130 Accurateness: 0.617000 Epoch thirteen, CIFAR-ten Batch 5: Loss: 0.6753 Accuracy: 0.620200 Epoch fourteen, CIFAR-10 Batch one: Loss: 0.7752 Accuracy: 0.615800 Epoch 14, CIFAR-10 Batch 2: Loss: 0.7367 Accuracy: 0.611200 Epoch 14, CIFAR-10 Batch three: Loss: 0.6341 Accuracy: 0.617400 Epoch 14, CIFAR-ten Batch four: Loss: 0.6571 Accurateness: 0.619200 Epoch 14, CIFAR-10 Batch 5: Loss: 0.6387 Accurateness: 0.618400 Epoch 15, CIFAR-10 Batch one: Loss: 0.7081 Accuracy: 0.618000 Epoch 15, CIFAR-10 Batch 2: Loss: 0.6640 Accuracy: 0.618800 Epoch fifteen, CIFAR-ten Batch three: Loss: 0.6042 Accuracy: 0.620200 Epoch fifteen, CIFAR-10 Batch iv: Loss: 0.6425 Accuracy: 0.623400 Epoch 15, CIFAR-10 Batch five: Loss: 0.6315 Accuracy: 0.615800 Epoch xvi, CIFAR-x Batch 1: Loss: 0.6888 Accuracy: 0.622400 Epoch 16, CIFAR-10 Batch 2: Loss: 0.6402 Accuracy: 0.629000 Epoch 16, CIFAR-ten Batch 3: Loss: 0.6064 Accuracy: 0.622200 Epoch 16, CIFAR-10 Batch iv: Loss: 0.6199 Accuracy: 0.628200 Epoch 16, CIFAR-10 Batch v: Loss: 0.5833 Accuracy: 0.627200 Epoch 17, CIFAR-10 Batch 1: Loss: 0.6609 Accuracy: 0.628400 Epoch 17, CIFAR-x Batch 2: Loss: 0.6440 Accuracy: 0.623600 Epoch 17, CIFAR-x Batch 3: Loss: 0.5946 Accurateness: 0.618400 Epoch 17, CIFAR-10 Batch 4: Loss: 0.6051 Accuracy: 0.625800 Epoch 17, CIFAR-x Batch v: Loss: 0.5744 Accuracy: 0.623800 Epoch 18, CIFAR-10 Batch 1: Loss: 0.6245 Accurateness: 0.625600 Epoch 18, CIFAR-10 Batch ii: Loss: 0.6122 Accuracy: 0.629800 Epoch 18, CIFAR-10 Batch 3: Loss: 0.5631 Accurateness: 0.627600 Epoch 18, CIFAR-10 Batch 4: Loss: 0.5838 Accuracy: 0.624800 Epoch 18, CIFAR-x Batch 5: Loss: 0.5398 Accuracy: 0.626800 Epoch 19, CIFAR-ten Batch ane: Loss: 0.6303 Accuracy: 0.626800 Epoch 19, CIFAR-x Batch 2: Loss: 0.6007 Accuracy: 0.629600 Epoch xix, CIFAR-10 Batch 3: Loss: 0.5293 Accurateness: 0.629600 Epoch nineteen, CIFAR-10 Batch four: Loss: 0.5686 Accuracy: 0.627600 Epoch 19, CIFAR-10 Batch five: Loss: 0.5145 Accuracy: 0.631000 Epoch 20, CIFAR-10 Batch 1: Loss: 0.5765 Accuracy: 0.633200 Epoch 20, CIFAR-10 Batch 2: Loss: 0.5736 Accuracy: 0.631600 Epoch xx, CIFAR-10 Batch iii: Loss: 0.5330 Accuracy: 0.624200 Epoch 20, CIFAR-x Batch 4: Loss: 0.5316 Accuracy: 0.635000 Epoch 20, CIFAR-10 Batch v: Loss: 0.5134 Accuracy: 0.627800 Epoch 21, CIFAR-10 Batch 1: Loss: 0.5657 Accuracy: 0.634400 Epoch 21, CIFAR-10 Batch 2: Loss: 0.5481 Accuracy: 0.632000 Epoch 21, CIFAR-10 Batch three: Loss: 0.4640 Accuracy: 0.640600 Epoch 21, CIFAR-10 Batch iv: Loss: 0.5179 Accuracy: 0.627400 Epoch 21, CIFAR-10 Batch 5: Loss: 0.4900 Accurateness: 0.628000 Epoch 22, CIFAR-x Batch 1: Loss: 0.5310 Accuracy: 0.635400 Epoch 22, CIFAR-10 Batch two: Loss: 0.5021 Accuracy: 0.639200 Epoch 22, CIFAR-10 Batch 3: Loss: 0.4728 Accuracy: 0.633400 Epoch 22, CIFAR-10 Batch four: Loss: 0.5182 Accuracy: 0.631400 Epoch 22, CIFAR-ten Batch five: Loss: 0.4858 Accuracy: 0.627800 Epoch 23, CIFAR-10 Batch 1: Loss: 0.5141 Accuracy: 0.632600 Epoch 23, CIFAR-10 Batch ii: Loss: 0.4982 Accurateness: 0.629800 Epoch 23, CIFAR-10 Batch iii: Loss: 0.4405 Accuracy: 0.635800 Epoch 23, CIFAR-10 Batch 4: Loss: 0.4856 Accuracy: 0.629000 Epoch 23, CIFAR-10 Batch 5: Loss: 0.4512 Accuracy: 0.631000 Epoch 24, CIFAR-10 Batch 1: Loss: 0.4858 Accurateness: 0.639200 Epoch 24, CIFAR-10 Batch ii: Loss: 0.4674 Accuracy: 0.640000 Epoch 24, CIFAR-ten Batch 3: Loss: 0.4449 Accurateness: 0.638600 Epoch 24, CIFAR-x Batch iv: Loss: 0.5062 Accuracy: 0.630400 Epoch 24, CIFAR-10 Batch 5: Loss: 0.4316 Accuracy: 0.638800 Epoch 25, CIFAR-x Batch 1: Loss: 0.5093 Accuracy: 0.633000 Epoch 25, CIFAR-10 Batch 2: Loss: 0.4663 Accuracy: 0.639200 Epoch 25, CIFAR-10 Batch iii: Loss: 0.4213 Accuracy: 0.633800 Epoch 25, CIFAR-10 Batch iv: Loss: 0.4414 Accuracy: 0.640400 Epoch 25, CIFAR-ten Batch 5: Loss: 0.3919 Accuracy: 0.639000 Epoch 26, CIFAR-x Batch 1: Loss: 0.4604 Accuracy: 0.633600 Epoch 26, CIFAR-10 Batch ii: Loss: 0.4478 Accuracy: 0.639600 Epoch 26, CIFAR-ten Batch 3: Loss: 0.4152 Accuracy: 0.630600 Epoch 26, CIFAR-ten Batch 4: Loss: 0.4331 Accuracy: 0.632200 Epoch 26, CIFAR-x Batch 5: Loss: 0.3908 Accuracy: 0.636400 Epoch 27, CIFAR-10 Batch 1: Loss: 0.4586 Accuracy: 0.638200 Epoch 27, CIFAR-10 Batch two: Loss: 0.4317 Accuracy: 0.635800 Epoch 27, CIFAR-ten Batch 3: Loss: 0.3911 Accuracy: 0.642200 Epoch 27, CIFAR-x Batch iv: Loss: 0.4453 Accuracy: 0.639800 Epoch 27, CIFAR-ten Batch 5: Loss: 0.3741 Accurateness: 0.645200 Epoch 28, CIFAR-10 Batch 1: Loss: 0.4130 Accuracy: 0.642400 Epoch 28, CIFAR-ten Batch 2: Loss: 0.4123 Accurateness: 0.639800 Epoch 28, CIFAR-10 Batch iii: Loss: 0.3659 Accuracy: 0.640000 Epoch 28, CIFAR-10 Batch 4: Loss: 0.3900 Accuracy: 0.638400 Epoch 28, CIFAR-x Batch v: Loss: 0.3771 Accuracy: 0.637800 Epoch 29, CIFAR-10 Batch ane: Loss: 0.4148 Accuracy: 0.637800 Epoch 29, CIFAR-10 Batch ii: Loss: 0.3832 Accurateness: 0.640600 Epoch 29, CIFAR-10 Batch 3: Loss: 0.3420 Accuracy: 0.638800 Epoch 29, CIFAR-10 Batch 4: Loss: 0.3820 Accuracy: 0.638400 Epoch 29, CIFAR-ten Batch five: Loss: 0.3723 Accuracy: 0.639200 Epoch 30, CIFAR-x Batch 1: Loss: 0.4162 Accuracy: 0.640800 Epoch 30, CIFAR-10 Batch 2: Loss: 0.3965 Accuracy: 0.639600 Epoch 30, CIFAR-ten Batch 3: Loss: 0.3554 Accuracy: 0.633600 Epoch 30, CIFAR-10 Batch 4: Loss: 0.3849 Accurateness: 0.636200 Epoch 30, CIFAR-10 Batch 5: Loss: 0.3422 Accuracy: 0.636200 Epoch 31, CIFAR-10 Batch 1: Loss: 0.4156 Accurateness: 0.637600 Epoch 31, CIFAR-10 Batch 2: Loss: 0.3760 Accuracy: 0.641200 Epoch 31, CIFAR-10 Batch iii: Loss: 0.3256 Accuracy: 0.639200 Epoch 31, CIFAR-10 Batch 4: Loss: 0.3667 Accuracy: 0.643000 Epoch 31, CIFAR-ten Batch 5: Loss: 0.3383 Accuracy: 0.649200 Epoch 32, CIFAR-x Batch 1: Loss: 0.3898 Accuracy: 0.640200 Epoch 32, CIFAR-ten Batch 2: Loss: 0.3838 Accuracy: 0.639200 Epoch 32, CIFAR-10 Batch 3: Loss: 0.3237 Accuracy: 0.629000 Epoch 32, CIFAR-x Batch iv: Loss: 0.3764 Accuracy: 0.633400 Epoch 32, CIFAR-10 Batch 5: Loss: 0.3289 Accuracy: 0.644000 Epoch 33, CIFAR-10 Batch one: Loss: 0.3508 Accuracy: 0.647000 Epoch 33, CIFAR-10 Batch ii: Loss: 0.3750 Accurateness: 0.645400 Epoch 33, CIFAR-10 Batch 3: Loss: 0.3186 Accuracy: 0.637400 Epoch 33, CIFAR-10 Batch 4: Loss: 0.3607 Accuracy: 0.636400 Epoch 33, CIFAR-10 Batch five: Loss: 0.3161 Accuracy: 0.653200 Epoch 34, CIFAR-10 Batch one: Loss: 0.3506 Accuracy: 0.647600 Epoch 34, CIFAR-x Batch 2: Loss: 0.3162 Accurateness: 0.646800 Epoch 34, CIFAR-10 Batch 3: Loss: 0.2843 Accuracy: 0.639800 Epoch 34, CIFAR-10 Batch 4: Loss: 0.3444 Accuracy: 0.648600 Epoch 34, CIFAR-ten Batch 5: Loss: 0.3132 Accurateness: 0.641600 Epoch 35, CIFAR-ten Batch ane: Loss: 0.3579 Accuracy: 0.644600 Epoch 35, CIFAR-10 Batch 2: Loss: 0.3347 Accuracy: 0.643200 Epoch 35, CIFAR-10 Batch 3: Loss: 0.2736 Accuracy: 0.644200 Epoch 35, CIFAR-ten Batch 4: Loss: 0.3252 Accuracy: 0.643600 Epoch 35, CIFAR-x Batch 5: Loss: 0.3019 Accuracy: 0.643000 Epoch 36, CIFAR-10 Batch 1: Loss: 0.3370 Accuracy: 0.642400 Epoch 36, CIFAR-10 Batch 2: Loss: 0.3228 Accuracy: 0.648200 Epoch 36, CIFAR-10 Batch 3: Loss: 0.2758 Accuracy: 0.643600 Epoch 36, CIFAR-10 Batch 4: Loss: 0.3151 Accuracy: 0.640400 Epoch 36, CIFAR-10 Batch 5: Loss: 0.2788 Accuracy: 0.650400 Epoch 37, CIFAR-ten Batch i: Loss: 0.3332 Accurateness: 0.648400 Epoch 37, CIFAR-10 Batch ii: Loss: 0.3011 Accuracy: 0.645800 Epoch 37, CIFAR-10 Batch 3: Loss: 0.2574 Accuracy: 0.646000 Epoch 37, CIFAR-10 Batch 4: Loss: 0.3147 Accuracy: 0.636000 Epoch 37, CIFAR-10 Batch v: Loss: 0.2663 Accuracy: 0.646400 Epoch 38, CIFAR-10 Batch 1: Loss: 0.3134 Accuracy: 0.646800 Epoch 38, CIFAR-x Batch 2: Loss: 0.2965 Accurateness: 0.642400 Epoch 38, CIFAR-ten Batch three: Loss: 0.2557 Accuracy: 0.648200 Epoch 38, CIFAR-10 Batch four: Loss: 0.2867 Accurateness: 0.648000 Epoch 38, CIFAR-10 Batch 5: Loss: 0.2640 Accurateness: 0.649000 Epoch 39, CIFAR-10 Batch 1: Loss: 0.2981 Accuracy: 0.645800 Epoch 39, CIFAR-10 Batch 2: Loss: 0.2709 Accuracy: 0.644200 Epoch 39, CIFAR-ten Batch 3: Loss: 0.2555 Accuracy: 0.639200 Epoch 39, CIFAR-ten Batch 4: Loss: 0.2893 Accurateness: 0.634800 Epoch 39, CIFAR-10 Batch 5: Loss: 0.2596 Accuracy: 0.648000 Epoch 40, CIFAR-10 Batch one: Loss: 0.3064 Accuracy: 0.650400 Epoch 40, CIFAR-10 Batch two: Loss: 0.3002 Accuracy: 0.649800 Epoch xl, CIFAR-ten Batch iii: Loss: 0.2426 Accuracy: 0.647000 Epoch 40, CIFAR-ten Batch 4: Loss: 0.2889 Accuracy: 0.639200 Epoch 40, CIFAR-10 Batch five: Loss: 0.2448 Accuracy: 0.638400 Epoch 41, CIFAR-10 Batch 1: Loss: 0.2979 Accuracy: 0.641000 Epoch 41, CIFAR-10 Batch 2: Loss: 0.2779 Accuracy: 0.642800 Epoch 41, CIFAR-ten Batch 3: Loss: 0.2511 Accuracy: 0.643200 Epoch 41, CIFAR-10 Batch 4: Loss: 0.2876 Accuracy: 0.646800 Epoch 41, CIFAR-10 Batch 5: Loss: 0.2440 Accuracy: 0.645000 Epoch 42, CIFAR-x Batch one: Loss: 0.2912 Accuracy: 0.643200 Epoch 42, CIFAR-ten Batch two: Loss: 0.2757 Accuracy: 0.642600 Epoch 42, CIFAR-ten Batch 3: Loss: 0.2373 Accuracy: 0.637400 Epoch 42, CIFAR-10 Batch 4: Loss: 0.2728 Accuracy: 0.642400 Epoch 42, CIFAR-ten Batch 5: Loss: 0.2491 Accuracy: 0.643600 Epoch 43, CIFAR-10 Batch ane: Loss: 0.2857 Accuracy: 0.641800 Epoch 43, CIFAR-10 Batch 2: Loss: 0.2747 Accurateness: 0.645400 Epoch 43, CIFAR-x Batch 3: Loss: 0.2328 Accuracy: 0.638600 Epoch 43, CIFAR-10 Batch 4: Loss: 0.2710 Accuracy: 0.643600 Epoch 43, CIFAR-10 Batch v: Loss: 0.2314 Accuracy: 0.646800 Epoch 44, CIFAR-x Batch 1: Loss: 0.2763 Accuracy: 0.645200 Epoch 44, CIFAR-x Batch ii: Loss: 0.2599 Accuracy: 0.648200 Epoch 44, CIFAR-ten Batch 3: Loss: 0.2357 Accurateness: 0.637800 Epoch 44, CIFAR-10 Batch four: Loss: 0.2540 Accurateness: 0.639800 Epoch 44, CIFAR-10 Batch 5: Loss: 0.2198 Accuracy: 0.645800 Epoch 45, CIFAR-10 Batch i: Loss: 0.2516 Accuracy: 0.641800 Epoch 45, CIFAR-10 Batch two: Loss: 0.2284 Accuracy: 0.643800 Epoch 45, CIFAR-x Batch 3: Loss: 0.1880 Accurateness: 0.640000 Epoch 45, CIFAR-x Batch four: Loss: 0.2301 Accuracy: 0.651800 Epoch 45, CIFAR-10 Batch 5: Loss: 0.2108 Accuracy: 0.643400 Epoch 46, CIFAR-10 Batch one: Loss: 0.2676 Accurateness: 0.641600 Epoch 46, CIFAR-ten Batch two: Loss: 0.2158 Accuracy: 0.649000 Epoch 46, CIFAR-10 Batch 3: Loss: 0.1938 Accuracy: 0.639400 Epoch 46, CIFAR-ten Batch 4: Loss: 0.2385 Accuracy: 0.645600 Epoch 46, CIFAR-10 Batch 5: Loss: 0.2036 Accurateness: 0.651600 Epoch 47, CIFAR-x Batch 1: Loss: 0.2381 Accuracy: 0.650600 Epoch 47, CIFAR-10 Batch 2: Loss: 0.2394 Accuracy: 0.647600 Epoch 47, CIFAR-10 Batch 3: Loss: 0.1914 Accurateness: 0.641400 Epoch 47, CIFAR-x Batch iv: Loss: 0.2320 Accuracy: 0.644000 Epoch 47, CIFAR-ten Batch five: Loss: 0.1951 Accurateness: 0.646800 Epoch 48, CIFAR-x Batch 1: Loss: 0.2197 Accurateness: 0.643800 Epoch 48, CIFAR-10 Batch ii: Loss: 0.2042 Accuracy: 0.643400 Epoch 48, CIFAR-x Batch 3: Loss: 0.1731 Accurateness: 0.652000 Epoch 48, CIFAR-10 Batch 4: Loss: 0.2294 Accurateness: 0.646200 Epoch 48, CIFAR-x Batch 5: Loss: 0.2097 Accurateness: 0.653600 Epoch 49, CIFAR-10 Batch 1: Loss: 0.2312 Accurateness: 0.649600 Epoch 49, CIFAR-10 Batch two: Loss: 0.2296 Accuracy: 0.645800 Epoch 49, CIFAR-10 Batch three: Loss: 0.1836 Accuracy: 0.647200 Epoch 49, CIFAR-ten Batch iv: Loss: 0.2477 Accuracy: 0.635800 Epoch 49, CIFAR-x Batch 5: Loss: 0.2052 Accuracy: 0.643800 Epoch 50, CIFAR-ten Batch ane: Loss: 0.2350 Accuracy: 0.648400 Epoch 50, CIFAR-ten Batch 2: Loss: 0.2066 Accuracy: 0.645600 Epoch l, CIFAR-10 Batch 3: Loss: 0.1757 Accuracy: 0.644400 Epoch l, CIFAR-10 Batch 4: Loss: 0.2201 Accuracy: 0.645800 Epoch 50, CIFAR-10 Batch v: Loss: 0.1965 Accuracy: 0.647800 Epoch 51, CIFAR-10 Batch 1: Loss: 0.2111 Accuracy: 0.644600 Epoch 51, CIFAR-10 Batch ii: Loss: 0.2176 Accurateness: 0.640800 Epoch 51, CIFAR-10 Batch three: Loss: 0.1670 Accuracy: 0.644800 Epoch 51, CIFAR-ten Batch 4: Loss: 0.1956 Accuracy: 0.648800 Epoch 51, CIFAR-10 Batch five: Loss: 0.1914 Accurateness: 0.642400 Epoch 52, CIFAR-10 Batch 1: Loss: 0.1971 Accuracy: 0.644400 Epoch 52, CIFAR-10 Batch two: Loss: 0.2074 Accuracy: 0.641800 Epoch 52, CIFAR-10 Batch 3: Loss: 0.1613 Accuracy: 0.645600 Epoch 52, CIFAR-10 Batch iv: Loss: 0.1965 Accuracy: 0.649600 Epoch 52, CIFAR-ten Batch 5: Loss: 0.1840 Accuracy: 0.647200 Epoch 53, CIFAR-ten Batch 1: Loss: 0.2084 Accuracy: 0.647600 Epoch 53, CIFAR-ten Batch two: Loss: 0.1914 Accuracy: 0.640800 Epoch 53, CIFAR-x Batch three: Loss: 0.1568 Accuracy: 0.644800 Epoch 53, CIFAR-10 Batch 4: Loss: 0.1944 Accurateness: 0.649200 Epoch 53, CIFAR-10 Batch 5: Loss: 0.1941 Accuracy: 0.647200 Epoch 54, CIFAR-10 Batch 1: Loss: 0.2289 Accuracy: 0.639600 Epoch 54, CIFAR-10 Batch 2: Loss: 0.1845 Accurateness: 0.640600 Epoch 54, CIFAR-ten Batch three: Loss: 0.1704 Accuracy: 0.644000 Epoch 54, CIFAR-10 Batch iv: Loss: 0.2345 Accuracy: 0.640000 Epoch 54, CIFAR-x Batch 5: Loss: 0.1836 Accurateness: 0.647600 Epoch 55, CIFAR-10 Batch 1: Loss: 0.2113 Accuracy: 0.644000 Epoch 55, CIFAR-ten Batch two: Loss: 0.1782 Accuracy: 0.644000 Epoch 55, CIFAR-10 Batch iii: Loss: 0.1485 Accuracy: 0.641200 Epoch 55, CIFAR-10 Batch 4: Loss: 0.2055 Accuracy: 0.639000 Epoch 55, CIFAR-x Batch five: Loss: 0.1538 Accuracy: 0.651200 Epoch 56, CIFAR-10 Batch i: Loss: 0.1811 Accurateness: 0.653000 Epoch 56, CIFAR-10 Batch 2: Loss: 0.1663 Accuracy: 0.643800 Epoch 56, CIFAR-x Batch 3: Loss: 0.1414 Accuracy: 0.646000 Epoch 56, CIFAR-10 Batch 4: Loss: 0.1673 Accuracy: 0.642800 Epoch 56, CIFAR-10 Batch 5: Loss: 0.1513 Accuracy: 0.648200 Epoch 57, CIFAR-ten Batch one: Loss: 0.1877 Accuracy: 0.642400 Epoch 57, CIFAR-10 Batch ii: Loss: 0.1632 Accurateness: 0.641800 Epoch 57, CIFAR-x Batch 3: Loss: 0.1487 Accuracy: 0.643800 Epoch 57, CIFAR-10 Batch 4: Loss: 0.1802 Accurateness: 0.636400 Epoch 57, CIFAR-10 Batch 5: Loss: 0.1552 Accuracy: 0.644600 Epoch 58, CIFAR-10 Batch 1: Loss: 0.1829 Accuracy: 0.647000 Epoch 58, CIFAR-10 Batch two: Loss: 0.1553 Accuracy: 0.639600 Epoch 58, CIFAR-10 Batch 3: Loss: 0.1353 Accuracy: 0.645000 Epoch 58, CIFAR-ten Batch 4: Loss: 0.1612 Accuracy: 0.649200 Epoch 58, CIFAR-ten Batch 5: Loss: 0.1379 Accuracy: 0.647600 Epoch 59, CIFAR-10 Batch 1: Loss: 0.1779 Accuracy: 0.647600 Epoch 59, CIFAR-10 Batch 2: Loss: 0.1660 Accuracy: 0.644200 Epoch 59, CIFAR-10 Batch 3: Loss: 0.1243 Accuracy: 0.650400 Epoch 59, CIFAR-ten Batch 4: Loss: 0.1540 Accuracy: 0.649000 Epoch 59, CIFAR-10 Batch 5: Loss: 0.1380 Accuracy: 0.643600 Epoch sixty, CIFAR-10 Batch 1: Loss: 0.1857 Accuracy: 0.644800 Epoch 60, CIFAR-10 Batch 2: Loss: 0.1472 Accuracy: 0.649800 Epoch 60, CIFAR-ten Batch 3: Loss: 0.1314 Accuracy: 0.645800 Epoch 60, CIFAR-10 Batch 4: Loss: 0.1686 Accurateness: 0.645000 Epoch 60, CIFAR-10 Batch 5: Loss: 0.1324 Accuracy: 0.649800 Epoch 61, CIFAR-10 Batch 1: Loss: 0.1827 Accuracy: 0.641000 Epoch 61, CIFAR-10 Batch 2: Loss: 0.1623 Accuracy: 0.644600 Epoch 61, CIFAR-10 Batch iii: Loss: 0.1298 Accurateness: 0.651200 Epoch 61, CIFAR-x Batch 4: Loss: 0.1636 Accurateness: 0.643400 Epoch 61, CIFAR-10 Batch five: Loss: 0.1511 Accuracy: 0.642000 Epoch 62, CIFAR-x Batch 1: Loss: 0.1581 Accuracy: 0.638800 Epoch 62, CIFAR-10 Batch two: Loss: 0.1464 Accuracy: 0.648800 Epoch 62, CIFAR-10 Batch iii: Loss: 0.1168 Accurateness: 0.646600 Epoch 62, CIFAR-x Batch 4: Loss: 0.1606 Accuracy: 0.641600 Epoch 62, CIFAR-10 Batch 5: Loss: 0.1295 Accuracy: 0.648200 Epoch 63, CIFAR-10 Batch 1: Loss: 0.1594 Accuracy: 0.642200 Epoch 63, CIFAR-ten Batch two: Loss: 0.1563 Accurateness: 0.650000 Epoch 63, CIFAR-10 Batch three: Loss: 0.1115 Accuracy: 0.639800 Epoch 63, CIFAR-ten Batch iv: Loss: 0.1576 Accuracy: 0.647800 Epoch 63, CIFAR-10 Batch 5: Loss: 0.1277 Accurateness: 0.646600 Epoch 64, CIFAR-x Batch i: Loss: 0.1630 Accuracy: 0.639400 Epoch 64, CIFAR-10 Batch two: Loss: 0.1540 Accuracy: 0.644400 Epoch 64, CIFAR-10 Batch 3: Loss: 0.1135 Accuracy: 0.640400 Epoch 64, CIFAR-10 Batch 4: Loss: 0.1637 Accuracy: 0.646400 Epoch 64, CIFAR-x Batch v: Loss: 0.1279 Accuracy: 0.648600 Epoch 65, CIFAR-10 Batch 1: Loss: 0.1556 Accuracy: 0.643200 Epoch 65, CIFAR-x Batch two: Loss: 0.1384 Accuracy: 0.644400 Epoch 65, CIFAR-x Batch three: Loss: 0.1150 Accuracy: 0.641600 Epoch 65, CIFAR-10 Batch 4: Loss: 0.1539 Accuracy: 0.638000 Epoch 65, CIFAR-10 Batch 5: Loss: 0.1281 Accuracy: 0.650000 Epoch 66, CIFAR-10 Batch 1: Loss: 0.1637 Accuracy: 0.640200 Epoch 66, CIFAR-10 Batch two: Loss: 0.1428 Accuracy: 0.653800 Epoch 66, CIFAR-10 Batch 3: Loss: 0.1071 Accuracy: 0.644400 Epoch 66, CIFAR-x Batch 4: Loss: 0.1335 Accuracy: 0.647400 Epoch 66, CIFAR-10 Batch 5: Loss: 0.1269 Accuracy: 0.649800 Epoch 67, CIFAR-ten Batch 1: Loss: 0.1440 Accuracy: 0.638400 Epoch 67, CIFAR-10 Batch 2: Loss: 0.1370 Accuracy: 0.640800 Epoch 67, CIFAR-10 Batch 3: Loss: 0.1082 Accuracy: 0.646000 Epoch 67, CIFAR-10 Batch 4: Loss: 0.1344 Accuracy: 0.647400 Epoch 67, CIFAR-x Batch five: Loss: 0.1252 Accurateness: 0.640400 Epoch 68, CIFAR-ten Batch 1: Loss: 0.1485 Accuracy: 0.640000 Epoch 68, CIFAR-10 Batch 2: Loss: 0.1331 Accuracy: 0.647000 Epoch 68, CIFAR-x Batch iii: Loss: 0.1168 Accuracy: 0.638000 Epoch 68, CIFAR-x Batch 4: Loss: 0.1487 Accuracy: 0.640600 Epoch 68, CIFAR-10 Batch 5: Loss: 0.1344 Accuracy: 0.646400 Epoch 69, CIFAR-10 Batch 1: Loss: 0.1364 Accuracy: 0.646400 Epoch 69, CIFAR-x Batch 2: Loss: 0.1286 Accuracy: 0.642600 Epoch 69, CIFAR-10 Batch iii: Loss: 0.1026 Accurateness: 0.646400 Epoch 69, CIFAR-10 Batch iv: Loss: 0.1465 Accurateness: 0.638000 Epoch 69, CIFAR-ten Batch 5: Loss: 0.1108 Accuracy: 0.647200 Epoch 70, CIFAR-x Batch 1: Loss: 0.1384 Accuracy: 0.645400 Epoch 70, CIFAR-10 Batch 2: Loss: 0.1215 Accuracy: 0.641800 Epoch lxx, CIFAR-10 Batch iii: Loss: 0.1033 Accuracy: 0.649400 Epoch 70, CIFAR-ten Batch 4: Loss: 0.1407 Accuracy: 0.638600 Epoch 70, CIFAR-ten Batch 5: Loss: 0.1062 Accuracy: 0.647000 Epoch 71, CIFAR-10 Batch one: Loss: 0.1554 Accuracy: 0.641600 Epoch 71, CIFAR-x Batch 2: Loss: 0.1250 Accurateness: 0.641200 Epoch 71, CIFAR-ten Batch 3: Loss: 0.0986 Accuracy: 0.643000 Epoch 71, CIFAR-10 Batch 4: Loss: 0.1333 Accuracy: 0.641200 Epoch 71, CIFAR-10 Batch 5: Loss: 0.1015 Accuracy: 0.643800 Epoch 72, CIFAR-10 Batch 1: Loss: 0.1538 Accurateness: 0.632400 Epoch 72, CIFAR-10 Batch 2: Loss: 0.1152 Accuracy: 0.649400 Epoch 72, CIFAR-ten Batch 3: Loss: 0.1011 Accuracy: 0.646200 Epoch 72, CIFAR-10 Batch 4: Loss: 0.1397 Accuracy: 0.638200 Epoch 72, CIFAR-10 Batch 5: Loss: 0.1120 Accurateness: 0.642400 Epoch 73, CIFAR-10 Batch 1: Loss: 0.1166 Accuracy: 0.644000 Epoch 73, CIFAR-10 Batch 2: Loss: 0.1250 Accuracy: 0.637800 Epoch 73, CIFAR-x Batch 3: Loss: 0.0888 Accuracy: 0.646200 Epoch 73, CIFAR-10 Batch 4: Loss: 0.1475 Accuracy: 0.639600 Epoch 73, CIFAR-10 Batch 5: Loss: 0.1135 Accuracy: 0.645400 Epoch 74, CIFAR-ten Batch 1: Loss: 0.1193 Accuracy: 0.653000 Epoch 74, CIFAR-10 Batch 2: Loss: 0.1113 Accuracy: 0.650200 Epoch 74, CIFAR-10 Batch 3: Loss: 0.0941 Accurateness: 0.646200 Epoch 74, CIFAR-10 Batch 4: Loss: 0.1470 Accuracy: 0.633400 Epoch 74, CIFAR-10 Batch 5: Loss: 0.1081 Accuracy: 0.645000 Epoch 75, CIFAR-10 Batch 1: Loss: 0.1306 Accuracy: 0.644400 Epoch 75, CIFAR-10 Batch 2: Loss: 0.1100 Accurateness: 0.643200 Epoch 75, CIFAR-10 Batch 3: Loss: 0.0893 Accuracy: 0.646200 Epoch 75, CIFAR-10 Batch 4: Loss: 0.1299 Accuracy: 0.637000 Epoch 75, CIFAR-10 Batch v: Loss: 0.1108 Accuracy: 0.645000 Epoch 76, CIFAR-10 Batch 1: Loss: 0.1293 Accuracy: 0.635800 Epoch 76, CIFAR-10 Batch 2: Loss: 0.1143 Accurateness: 0.645400 Epoch 76, CIFAR-10 Batch iii: Loss: 0.0916 Accuracy: 0.638600 Epoch 76, CIFAR-ten Batch 4: Loss: 0.1400 Accuracy: 0.635400 Epoch 76, CIFAR-10 Batch 5: Loss: 0.0966 Accuracy: 0.647600 Epoch 77, CIFAR-ten Batch 1: Loss: 0.1311 Accuracy: 0.646000 Epoch 77, CIFAR-x Batch ii: Loss: 0.0966 Accurateness: 0.648400 Epoch 77, CIFAR-x Batch three: Loss: 0.0824 Accuracy: 0.642600 Epoch 77, CIFAR-10 Batch 4: Loss: 0.1455 Accuracy: 0.636600 Epoch 77, CIFAR-ten Batch 5: Loss: 0.1105 Accuracy: 0.646200 Epoch 78, CIFAR-ten Batch 1: Loss: 0.1304 Accurateness: 0.639200 Epoch 78, CIFAR-10 Batch 2: Loss: 0.1124 Accurateness: 0.641600 Epoch 78, CIFAR-10 Batch 3: Loss: 0.0948 Accuracy: 0.644400 Epoch 78, CIFAR-10 Batch 4: Loss: 0.1098 Accurateness: 0.647200 Epoch 78, CIFAR-x Batch 5: Loss: 0.0951 Accuracy: 0.646000 Epoch 79, CIFAR-x Batch 1: Loss: 0.1276 Accuracy: 0.642200 Epoch 79, CIFAR-10 Batch two: Loss: 0.1163 Accuracy: 0.639400 Epoch 79, CIFAR-10 Batch three: Loss: 0.1029 Accuracy: 0.641400 Epoch 79, CIFAR-10 Batch 4: Loss: 0.1206 Accuracy: 0.644200 Epoch 79, CIFAR-x Batch v: Loss: 0.1067 Accurateness: 0.646000 Epoch eighty, CIFAR-10 Batch 1: Loss: 0.1045 Accuracy: 0.650000 Epoch 80, CIFAR-ten Batch 2: Loss: 0.1078 Accuracy: 0.645600 Epoch fourscore, CIFAR-10 Batch three: Loss: 0.0914 Accuracy: 0.639600 Epoch eighty, CIFAR-x Batch iv: Loss: 0.1260 Accuracy: 0.638600 Epoch 80, CIFAR-10 Batch 5: Loss: 0.0965 Accuracy: 0.643600 Epoch 81, CIFAR-ten Batch 1: Loss: 0.0976 Accuracy: 0.649800 Epoch 81, CIFAR-10 Batch 2: Loss: 0.1145 Accurateness: 0.642200 Epoch 81, CIFAR-10 Batch 3: Loss: 0.0889 Accuracy: 0.642400 Epoch 81, CIFAR-ten Batch iv: Loss: 0.0994 Accuracy: 0.641400 Epoch 81, CIFAR-x Batch 5: Loss: 0.0971 Accuracy: 0.647200 Epoch 82, CIFAR-10 Batch 1: Loss: 0.1189 Accuracy: 0.645200 Epoch 82, CIFAR-10 Batch 2: Loss: 0.1055 Accuracy: 0.639800 Epoch 82, CIFAR-10 Batch iii: Loss: 0.1099 Accuracy: 0.643000 Epoch 82, CIFAR-ten Batch 4: Loss: 0.1160 Accuracy: 0.640600 Epoch 82, CIFAR-10 Batch 5: Loss: 0.0981 Accuracy: 0.642200 Epoch 83, CIFAR-10 Batch one: Loss: 0.1286 Accuracy: 0.638400 Epoch 83, CIFAR-10 Batch 2: Loss: 0.1067 Accuracy: 0.640200 Epoch 83, CIFAR-10 Batch 3: Loss: 0.0995 Accuracy: 0.642200 Epoch 83, CIFAR-10 Batch four: Loss: 0.1063 Accurateness: 0.643000 Epoch 83, CIFAR-ten Batch 5: Loss: 0.0921 Accuracy: 0.651200 Epoch 84, CIFAR-10 Batch 1: Loss: 0.1054 Accuracy: 0.649800 Epoch 84, CIFAR-10 Batch two: Loss: 0.0995 Accuracy: 0.642400 Epoch 84, CIFAR-10 Batch 3: Loss: 0.0853 Accurateness: 0.641400 Epoch 84, CIFAR-10 Batch 4: Loss: 0.1148 Accuracy: 0.644000 Epoch 84, CIFAR-x Batch 5: Loss: 0.0890 Accuracy: 0.645200 Epoch 85, CIFAR-x Batch 1: Loss: 0.1156 Accuracy: 0.646600 Epoch 85, CIFAR-x Batch ii: Loss: 0.1030 Accuracy: 0.647200 Epoch 85, CIFAR-10 Batch 3: Loss: 0.0781 Accurateness: 0.635200 Epoch 85, CIFAR-10 Batch 4: Loss: 0.1015 Accuracy: 0.641600 Epoch 85, CIFAR-10 Batch five: Loss: 0.0850 Accuracy: 0.645400 Epoch 86, CIFAR-x Batch ane: Loss: 0.0958 Accuracy: 0.648600 Epoch 86, CIFAR-ten Batch 2: Loss: 0.0915 Accuracy: 0.644400 Epoch 86, CIFAR-10 Batch three: Loss: 0.0823 Accuracy: 0.637600 Epoch 86, CIFAR-10 Batch 4: Loss: 0.1023 Accuracy: 0.643800 Epoch 86, CIFAR-10 Batch five: Loss: 0.0944 Accuracy: 0.646800 Epoch 87, CIFAR-10 Batch ane: Loss: 0.1135 Accuracy: 0.642400 Epoch 87, CIFAR-10 Batch 2: Loss: 0.0949 Accurateness: 0.644800 Epoch 87, CIFAR-10 Batch 3: Loss: 0.0848 Accuracy: 0.641400 Epoch 87, CIFAR-10 Batch 4: Loss: 0.0948 Accuracy: 0.641000 Epoch 87, CIFAR-10 Batch 5: Loss: 0.0885 Accuracy: 0.640800 Epoch 88, CIFAR-10 Batch 1: Loss: 0.0974 Accuracy: 0.653400 Epoch 88, CIFAR-10 Batch 2: Loss: 0.0850 Accuracy: 0.645400 Epoch 88, CIFAR-10 Batch 3: Loss: 0.0773 Accurateness: 0.646800 Epoch 88, CIFAR-10 Batch iv: Loss: 0.0935 Accuracy: 0.642800 Epoch 88, CIFAR-10 Batch v: Loss: 0.0892 Accuracy: 0.643600 Epoch 89, CIFAR-10 Batch one: Loss: 0.0952 Accuracy: 0.646600 Epoch 89, CIFAR-ten Batch 2: Loss: 0.0907 Accuracy: 0.640400 Epoch 89, CIFAR-10 Batch iii: Loss: 0.0748 Accurateness: 0.644600 Epoch 89, CIFAR-ten Batch 4: Loss: 0.0819 Accuracy: 0.639000 Epoch 89, CIFAR-10 Batch five: Loss: 0.0956 Accuracy: 0.644800 Epoch 90, CIFAR-10 Batch 1: Loss: 0.0865 Accurateness: 0.648400 Epoch 90, CIFAR-ten Batch ii: Loss: 0.0927 Accuracy: 0.638000 Epoch xc, CIFAR-10 Batch 3: Loss: 0.0713 Accurateness: 0.638400 Epoch xc, CIFAR-10 Batch 4: Loss: 0.0867 Accuracy: 0.644200 Epoch xc, CIFAR-10 Batch 5: Loss: 0.0855 Accuracy: 0.646600 Epoch 91, CIFAR-x Batch 1: Loss: 0.0994 Accurateness: 0.637400 Epoch 91, CIFAR-10 Batch 2: Loss: 0.0858 Accuracy: 0.640200 Epoch 91, CIFAR-10 Batch 3: Loss: 0.0784 Accuracy: 0.643000 Epoch 91, CIFAR-10 Batch iv: Loss: 0.0849 Accuracy: 0.638000 Epoch 91, CIFAR-ten Batch v: Loss: 0.0871 Accuracy: 0.647600 Epoch 92, CIFAR-10 Batch 1: Loss: 0.0916 Accurateness: 0.644600 Epoch 92, CIFAR-ten Batch 2: Loss: 0.0733 Accuracy: 0.645600 Epoch 92, CIFAR-x Batch three: Loss: 0.0712 Accuracy: 0.639200 Epoch 92, CIFAR-10 Batch 4: Loss: 0.0814 Accurateness: 0.646000 Epoch 92, CIFAR-10 Batch v: Loss: 0.0808 Accuracy: 0.645400 Epoch 93, CIFAR-10 Batch 1: Loss: 0.0920 Accurateness: 0.643000 Epoch 93, CIFAR-x Batch 2: Loss: 0.0800 Accuracy: 0.645400 Epoch 93, CIFAR-10 Batch 3: Loss: 0.0669 Accuracy: 0.639800 Epoch 93, CIFAR-10 Batch 4: Loss: 0.0856 Accurateness: 0.634200 Epoch 93, CIFAR-10 Batch 5: Loss: 0.0888 Accuracy: 0.641200 Epoch 94, CIFAR-10 Batch 1: Loss: 0.0891 Accuracy: 0.643000 Epoch 94, CIFAR-ten Batch ii: Loss: 0.0797 Accurateness: 0.644400 Epoch 94, CIFAR-ten Batch 3: Loss: 0.0613 Accuracy: 0.645200 Epoch 94, CIFAR-10 Batch 4: Loss: 0.0875 Accurateness: 0.637800 Epoch 94, CIFAR-10 Batch five: Loss: 0.0848 Accuracy: 0.642600 Epoch 95, CIFAR-ten Batch 1: Loss: 0.0824 Accurateness: 0.644000 Epoch 95, CIFAR-ten Batch 2: Loss: 0.0818 Accuracy: 0.636400 Epoch 95, CIFAR-10 Batch 3: Loss: 0.0689 Accuracy: 0.637800 Epoch 95, CIFAR-ten Batch 4: Loss: 0.0829 Accurateness: 0.637000 Epoch 95, CIFAR-ten Batch 5: Loss: 0.0779 Accuracy: 0.645200 Epoch 96, CIFAR-10 Batch 1: Loss: 0.0971 Accurateness: 0.642400 Epoch 96, CIFAR-x Batch 2: Loss: 0.0904 Accuracy: 0.640200 Epoch 96, CIFAR-x Batch iii: Loss: 0.0733 Accurateness: 0.637600 Epoch 96, CIFAR-10 Batch 4: Loss: 0.0846 Accuracy: 0.637200 Epoch 96, CIFAR-ten Batch 5: Loss: 0.0699 Accurateness: 0.639600 Epoch 97, CIFAR-ten Batch 1: Loss: 0.0889 Accuracy: 0.644800 Epoch 97, CIFAR-10 Batch 2: Loss: 0.0786 Accuracy: 0.635800 Epoch 97, CIFAR-10 Batch three: Loss: 0.0856 Accuracy: 0.641400 Epoch 97, CIFAR-10 Batch 4: Loss: 0.0821 Accuracy: 0.633000 Epoch 97, CIFAR-ten Batch 5: Loss: 0.0623 Accuracy: 0.645000 Epoch 98, CIFAR-10 Batch 1: Loss: 0.0856 Accurateness: 0.646600 Epoch 98, CIFAR-10 Batch ii: Loss: 0.0764 Accuracy: 0.646800 Epoch 98, CIFAR-10 Batch 3: Loss: 0.0556 Accurateness: 0.640200 Epoch 98, CIFAR-10 Batch iv: Loss: 0.0802 Accuracy: 0.630600 Epoch 98, CIFAR-10 Batch 5: Loss: 0.0740 Accuracy: 0.642800 Epoch 99, CIFAR-10 Batch 1: Loss: 0.0793 Accurateness: 0.644600 Epoch 99, CIFAR-x Batch 2: Loss: 0.0794 Accuracy: 0.644600 Epoch 99, CIFAR-10 Batch iii: Loss: 0.0755 Accuracy: 0.645000 Epoch 99, CIFAR-10 Batch iv: Loss: 0.0795 Accuracy: 0.634400 Epoch 99, CIFAR-10 Batch 5: Loss: 0.0820 Accurateness: 0.644200 Epoch 100, CIFAR-10 Batch 1: Loss: 0.0880 Accuracy: 0.643800 Epoch 100, CIFAR-10 Batch 2: Loss: 0.0739 Accuracy: 0.640800 Epoch 100, CIFAR-10 Batch iii: Loss: 0.0666 Accuracy: 0.640400 Epoch 100, CIFAR-10 Batch 4: Loss: 0.0792 Accurateness: 0.633800 Epoch 100, CIFAR-ten Batch 5: Loss: 0.0804 Accuracy: 0.645000 Checkpoint
The model has been saved to disk.
Examination Model
Test your model against the exam dataset. This will exist your final accuracy. Yous should have an accuracy greater than 50%. If you lot don't, keep tweaking the model compages and parameters.
""" DON'T MODIFY Annihilation IN THIS CELL """ % matplotlib inline % config InlineBackend.figure_format = 'retina' import tensorflow as tf import pickle import helper import random # Set batch size if not already set attempt: if batch_size: pass except NameError: batch_size = 64 save_model_path = './image_classification' n_samples = iv top_n_predictions = three def test_model(): """ Test the saved model confronting the test dataset """ test_features, test_labels = pickle.load(open('preprocess_test.p', mode = 'rb')) loaded_graph = tf.Graph() with tf.Session(graph = loaded_graph) equally sess: # Load model loader = tf.train.import_meta_graph(save_model_path + '.meta') loader.restore(sess, save_model_path) # Get Tensors from loaded model loaded_x = loaded_graph.get_tensor_by_name('10:0') loaded_y = loaded_graph.get_tensor_by_name('y:0') loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0') loaded_logits = loaded_graph.get_tensor_by_name('logits:0') loaded_acc = loaded_graph.get_tensor_by_name('accurateness:0') # Get accuracy in batches for retentivity limitations test_batch_acc_total = 0 test_batch_count = 0 for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size): test_batch_acc_total += sess.run( loaded_acc, feed_dict ={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0}) test_batch_count += 1 impress('Testing Accuracy: {}\n'.format(test_batch_acc_total / test_batch_count)) # Print Random Samples random_test_features, random_test_labels = tuple(zip(* random.sample(list(zip(test_features, test_labels)), n_samples))) random_test_predictions = sess.run( tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions), feed_dict ={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0}) helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions) test_model() INFO:tensorflow:Restoring parameters from ./image_classification Testing Accuracy: 0.6417566657066345 
Why fifty-fourscore% Accuracy?
You might be wondering why y'all can't go an accuracy any higher. First things first, 50% isn't bad for a elementary CNN. Pure guessing would get yous 10% accuracy. However, yous might notice people are getting scores well above 80%. That's considering we haven't taught yous all there is to know about neural networks. We still need to cover a few more techniques.
Submitting This Projection
When submitting this projection, brand sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_image_classification.ipynb" and salve information technology equally a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.
Source: https://github.com/ltpitt/python-jupyter-image-classification