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Tensorflow and the pre-trained model can be used for feature extraction by setting the ‘trainable’ feature of the previously created ‘base_model’ to ‘False’.

Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model. 

We will understand how to classify images of cats and dogs with the help of transfer learning from a pre-trained network. The intuition behind transfer learning for image classification is, if a model is trained on a large and general dataset, this model can be used to effectively serve as a generic model for the visual world. It would have learned the feature maps, which means the user won’t have to start from scratch by training a large model on a large dataset.

Read More: How can a customized model be pre-trained?

We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.

Example print("Feature extraction") base_model.trainable = False print("The base model architecture") The base model architecture Model: "mobilenetv2_1.00_160" __________________________________________________________________________________________________ Layer (type)                   Output Shape       Param #   Connected to ================================================================================================== input_1 (InputLayer)         [(None, 160, 160, 3)             0 __________________________________________________________________________________________________ Conv1 (Conv2D)              (None, 80, 80, 32)      864       input_1[0][0] __________________________________________________________________________________________________ bn_Conv1 (BatchNormalization) (None, 80, 80, 32)   128         Conv1[0][0] __________________________________________________________________________________________________ Conv1_relu (ReLU)           (None, 80, 80, 32)       0       bn_Conv1[0][0] __________________________________________________________________________________________________ expanded_conv_depthwise (Depthw (None, 80, 80, 32)   288         Conv1_relu[0][0] __________________________________________________________________________________________________ expanded_conv_depthwise_BN (Bat (None, 80, 80, 32)   128       expanded_conv_depthwise[0][0] __________________________________________________________________________________________________ expanded_conv_depthwise_relu (R (None, 80, 80, 32)    0       expanded_conv_depthwise_BN[0][0] __________________________________________________________________________________________________ expanded_conv_project (Conv2D)  (None, 80, 80, 16)   512           expanded_conv_depthwise_relu[0][0 __________________________________________________________________________________________________ expanded_conv_project_BN (Batch (None, 80, 80, 16)      64       expanded_conv_project[0][0] __________________________________________________________________________________________________ block_1_expand (Conv2D)    (None, 80, 80, 96)         1536         expanded_conv_project_BN[0][0] __________________________________________________________________________________________________ block_1_expand_BN (BatchNormali  (None, 80, 80, 96)     384            block_1_expand[0][0] __________________________________________________________________________________________________ block_1_expand_relu (ReLU)  (None, 80, 80, 96)        0            block_1_expand_BN[0][0] __________________________________________________________________________________________________ block_1_pad (ZeroPadding2D)  (None, 81, 81, 96)     0            block_1_expand_relu[0][0] __________________________________________________________________________________________________ block_1_depthwise (DepthwiseCon (None, 40, 40, 96)   864           block_1_pad[0][0] __________________________________________________________________________________________________ block_1_depthwise_BN (BatchNorm (None, 40, 40, 96)   384          block_1_depthwise[0][0] __________________________________________________________________________________________________ block_1_depthwise_relu (ReLU)   (None, 40, 40, 96)   0             block_1_depthwise_BN[0][0] __________________________________________________________________________________________________ block_1_project (Conv2D)     (None, 40, 40, 24)    2304           block_1_depthwise_relu[0][0] __________________________________________________________________________________________________ block_1_project_BN (BatchNormal  (None, 40, 40, 24)   96          block_1_project[0][0] __________________________________________________________________________________________________ block_2_expand (Conv2D) (None, 40, 40, 144)    3456           block_1_project_BN[0][0] __________________________________________________________________________________________________ block_2_expand_BN (BatchNormali (None, 40, 40, 144)   576          block_2_expand[0][0] __________________________________________________________________________________________________ block_2_expand_relu (ReLU) (None, 40, 40, 144)     0           block_2_expand_BN[0][0] __________________________________________________________________________________________________ block_2_depthwise (DepthwiseCon (None, 40, 40, 144)   1296       block_2_expand_relu[0][0] __________________________________________________________________________________________________ block_2_depthwise_BN (BatchNorm (None, 40, 40, 144)   576      block_2_depthwise[0][0] __________________________________________________________________________________________________ block_2_depthwise_relu (ReLU) (None, 40, 40, 144)    0        block_2_depthwise_BN[0][0] __________________________________________________________________________________________________ block_2_project (Conv2D)   (None, 40, 40, 24)      3456      block_2_depthwise_relu[0][0] __________________________________________________________________________________________________ block_2_project_BN (BatchNormal (None, 40, 40, 24)    96          block_2_project[0][0] __________________________________________________________________________________________________ block_2_add (Add)        (None, 40, 40, 24)         0         block_1_project_BN[0][0] block_2_project_BN[0][0] __________________________________________________________________________________________________ block_3_expand (Conv2D)      (None, 40, 40, 144)     3456         block_2_add[0][0] __________________________________________________________________________________________________ block_3_expand_BN (BatchNormali (None, 40, 40, 144)    576       block_3_expand[0][0] __________________________________________________________________________________________________ block_3_expand_relu (ReLU) (None, 40, 40, 144)        0       block_3_expand_BN[0][0] __________________________________________________________________________________________________ block_3_pad (ZeroPadding2D) (None, 41, 41, 144)   0          block_3_expand_relu[0][0] __________________________________________________________________________________________________ block_3_depthwise (DepthwiseCon (None, 20, 20, 144)  1296         block_3_pad[0][0] __________________________________________________________________________________________________ block_3_depthwise_BN (BatchNorm (None, 20, 20, 144)   576    block_3_depthwise[0][0] __________________________________________________________________________________________________ block_3_depthwise_relu (ReLU)   (None, 20, 20, 144)   0         block_3_depthwise_BN[0][0] __________________________________________________________________________________________________ block_3_project (Conv2D)   (None, 20, 20, 32)      4608          block_3_depthwise_relu[0][0] __________________________________________________________________________________________________ block_3_project_BN (BatchNormal  (None, 20, 20, 32)  128      block_3_project[0][0] __________________________________________________________________________________________________ block_4_expand (Conv2D)   (None, 20, 20, 192)     6144         block_3_project_BN[0][0] __________________________________________________________________________________________________ block_4_expand_BN (BatchNormali (None, 20, 20, 192)   768       block_4_expand[0][0] __________________________________________________________________________________________________ block_4_expand_relu (ReLU)   (None, 20, 20, 192)    0        block_4_expand_BN[0][0] __________________________________________________________________________________________________ block_4_depthwise (DepthwiseCon (None, 20, 20, 192)   1728       block_4_expand_relu[0][0] __________________________________________________________________________________________________ block_4_depthwise_BN (BatchNorm   (None, 20, 20, 192)    768       block_4_depthwise[0][0] __________________________________________________________________________________________________ block_4_depthwise_relu (ReLU)   (None, 20, 20, 192)     0         block_4_depthwise_BN[0][0] __________________________________________________________________________________________________ block_4_project (Conv2D)   (None, 20, 20, 32)        6144      block_4_depthwise_relu[0][0] __________________________________________________________________________________________________ block_4_project_BN (BatchNormal  (None, 20, 20, 32)   128        block_4_project[0][0] __________________________________________________________________________________________________ block_4_add (Add)         (None, 20, 20, 32)       0        block_3_project_BN[0][0] block_4_project_BN[0][0] __________________________________________________________________________________________________ block_5_expand (Conv2D)   (None, 20, 20, 192)      6144            block_4_add[0][0] __________________________________________________________________________________________________ block_5_expand_BN (BatchNormali (None, 20, 20, 192)   768         block_5_expand[0][0] __________________________________________________________________________________________________ block_5_expand_relu (ReLU) (None, 20, 20, 192)          0        block_5_expand_BN[0][0] __________________________________________________________________________________________________ block_5_depthwise (DepthwiseCon  (None, 20, 20, 192)   1728      block_5_expand_relu[0][0] __________________________________________________________________________________________________ block_5_depthwise_BN (BatchNorm (None, 20, 20, 192)   768       block_5_depthwise[0][0] __________________________________________________________________________________________________ block_5_depthwise_relu (ReLU)   (None, 20, 20, 192)      0       block_5_depthwise_BN[0][0] __________________________________________________________________________________________________ block_5_project (Conv2D)   (None, 20, 20, 32)         6144    block_5_depthwise_relu[0][0] __________________________________________________________________________________________________ block_5_project_BN (BatchNormal  (None, 20, 20, 32)   128       block_5_project[0][0] __________________________________________________________________________________________________ block_5_add (Add)           (None, 20, 20, 32)      0       block_4_add[0][0] block_5_project_BN[0][0] __________________________________________________________________________________________________ block_6_expand (Conv2D)     (None, 20, 20, 192)     6144          block_5_add[0][0] __________________________________________________________________________________________________ block_6_expand_BN (BatchNormali (None, 20, 20, 192)   768     block_6_expand[0][0] __________________________________________________________________________________________________ block_6_expand_relu (ReLU)   (None, 20, 20, 192)    0      block_6_expand_BN[0][0] __________________________________________________________________________________________________ block_6_pad (ZeroPadding2D)  (None, 21, 21, 192)   0       block_6_expand_relu[0][0] __________________________________________________________________________________________________ block_6_depthwise (DepthwiseCon (None, 10, 10, 192)   1728       block_6_pad[0][0] __________________________________________________________________________________________________ block_6_depthwise_BN (BatchNorm (None, 10, 10, 192)   768         block_6_depthwise[0][0] __________________________________________________________________________________________________ block_6_depthwise_relu (ReLU) (None, 10, 10, 192)   0    block_6_depthwise_BN[0][0] __________________________________________________________________________________________________ block_6_project (Conv2D) (None, 10, 10, 64)  12288         block_6_depthwise_relu[0][0] __________________________________________________________________________________________________ block_6_project_BN (BatchNormal (None, 10, 10, 64)   256        block_6_project[0][0] __________________________________________________________________________________________________ block_7_expand (Conv2D) (None, 10, 10, 384)        24576        block_6_project_BN[0][0] __________________________________________________________________________________________________ block_7_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_7_expand[0][0] __________________________________________________________________________________________________ block_7_expand_relu (ReLU) (None, 10, 10, 384)      0         block_7_expand_BN[0][0] __________________________________________________________________________________________________ block_7_depthwise (DepthwiseCon (None, 10, 10, 384)  3456       block_7_expand_relu[0][0] __________________________________________________________________________________________________ block_7_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536          block_7_depthwise[0][0] __________________________________________________________________________________________________ block_7_depthwise_relu (ReLU) (None, 10, 10, 384)    0            block_7_depthwise_BN[0][0] __________________________________________________________________________________________________ block_7_project (Conv2D) (None, 10, 10, 64)     24576          block_7_depthwise_relu[0][0] __________________________________________________________________________________________________ block_7_project_BN (BatchNormal (None, 10, 10, 64)   256            block_7_project[0][0] __________________________________________________________________________________________________ block_7_add (Add) (None, 10, 10, 64)          0               block_6_project_BN[0][0] block_7_project_BN[0][0] __________________________________________________________________________________________________ block_8_expand (Conv2D) (None, 10, 10, 384) 24576 block_7_add[0][0] __________________________________________________________________________________________________ block_8_expand_BN (BatchNormali (None, 10, 10, 384) 1536 block_8_expand[0][0] __________________________________________________________________________________________________ block_8_expand_relu (ReLU) (None, 10, 10, 384) 0 block_8_expand_BN[0][0] __________________________________________________________________________________________________ block_8_depthwise (DepthwiseCon (None, 10, 10, 384) 3456 block_8_expand_relu[0][0] __________________________________________________________________________________________________ block_8_depthwise_BN (BatchNorm (None, 10, 10, 384) 1536 block_8_depthwise[0][0] __________________________________________________________________________________________________ block_8_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_8_depthwise_BN[0][0] __________________________________________________________________________________________________ block_8_project (Conv2D) (None, 10, 10, 64) 24576 block_8_depthwise_relu[0][0] __________________________________________________________________________________________________ block_8_project_BN (BatchNormal (None, 10, 10, 64) 256 block_8_project[0][0] __________________________________________________________________________________________________ block_8_add (Add) (None, 10, 10, 64) 0 block_7_add[0][0] block_8_project_BN[0][0] __________________________________________________________________________________________________ block_9_expand (Conv2D) (None, 10, 10, 384) 24576 block_8_add[0][0] __________________________________________________________________________________________________ block_9_expand_BN (BatchNormali (None, 10, 10, 384) 1536 block_9_expand[0][0] __________________________________________________________________________________________________ block_9_expand_relu (ReLU) (None, 10, 10, 384) 0 block_9_expand_BN[0][0] __________________________________________________________________________________________________ block_9_depthwise (DepthwiseCon (None, 10, 10, 384) 3456 block_9_expand_relu[0][0] __________________________________________________________________________________________________ block_9_depthwise_BN (BatchNorm (None, 10, 10, 384) 1536 block_9_depthwise[0][0] __________________________________________________________________________________________________ block_9_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_9_depthwise_BN[0][0] __________________________________________________________________________________________________ block_9_project (Conv2D) (None, 10, 10, 64) 24576 block_9_depthwise_relu[0][0] __________________________________________________________________________________________________ block_9_project_BN (BatchNormal (None, 10, 10, 64) 256 block_9_project[0][0] __________________________________________________________________________________________________ block_9_add (Add) (None, 10, 10, 64) 0 block_8_add[0][0] block_9_project_BN[0][0] __________________________________________________________________________________________________ block_10_expand (Conv2D) (None, 10, 10, 384) 24576 block_9_add[0][0] __________________________________________________________________________________________________ block_10_expand_BN (BatchNormal (None, 10, 10, 384) 1536 block_10_expand[0][0] __________________________________________________________________________________________________ block_10_expand_relu (ReLU) (None, 10, 10, 384) 0 block_10_expand_BN[0][0] __________________________________________________________________________________________________ block_10_depthwise (DepthwiseCo (None, 10, 10, 384) 3456 block_10_expand_relu[0][0] __________________________________________________________________________________________________ block_10_depthwise_BN (BatchNor (None, 10, 10, 384) 1536 block_10_depthwise[0][0] __________________________________________________________________________________________________ block_10_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_10_depthwise_BN[0][0] __________________________________________________________________________________________________ block_10_project (Conv2D) (None, 10, 10, 96) 36864 block_10_depthwise_relu[0][0] __________________________________________________________________________________________________ block_10_project_BN (BatchNorma (None, 10, 10, 96) 384 block_10_project[0][0] __________________________________________________________________________________________________ block_11_expand (Conv2D) (None, 10, 10, 576) 55296 block_10_project_BN[0][0] __________________________________________________________________________________________________ block_11_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_11_expand[0][0] __________________________________________________________________________________________________ block_11_expand_relu (ReLU) (None, 10, 10, 576) 0 block_11_expand_BN[0][0] __________________________________________________________________________________________________ block_11_depthwise (DepthwiseCo (None, 10, 10, 576) 5184 block_11_expand_relu[0][0] __________________________________________________________________________________________________ block_11_depthwise_BN (BatchNor (None, 10, 10, 576) 2304 block_11_depthwise[0][0] __________________________________________________________________________________________________ block_11_depthwise_relu (ReLU) (None, 10, 10, 576) 0 block_11_depthwise_BN[0][0] __________________________________________________________________________________________________ block_11_project (Conv2D) (None, 10, 10, 96) 55296 block_11_depthwise_relu[0][0] __________________________________________________________________________________________________ block_11_project_BN (BatchNorma (None, 10, 10, 96) 384 block_11_project[0][0] __________________________________________________________________________________________________ block_11_add (Add) (None, 10, 10, 96) 0 block_10_project_BN[0][0] block_11_project_BN[0][0] __________________________________________________________________________________________________ block_12_expand (Conv2D) (None, 10, 10, 576) 55296 block_11_add[0][0] __________________________________________________________________________________________________ block_12_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_12_expand[0][0] __________________________________________________________________________________________________ block_12_expand_relu (ReLU) (None, 10, 10, 576) 0 block_12_expand_BN[0][0] __________________________________________________________________________________________________ block_12_depthwise (DepthwiseCo (None, 10, 10, 576) 5184 block_12_expand_relu[0][0] __________________________________________________________________________________________________ block_12_depthwise_BN (BatchNor (None, 10, 10, 576) 2304 block_12_depthwise[0][0] __________________________________________________________________________________________________ block_12_depthwise_relu (ReLU) (None, 10, 10, 576) 0 block_12_depthwise_BN[0][0] __________________________________________________________________________________________________ block_12_project (Conv2D) (None, 10, 10, 96) 55296 block_12_depthwise_relu[0][0] __________________________________________________________________________________________________ block_12_project_BN (BatchNorma (None, 10, 10, 96) 384 block_12_project[0][0] __________________________________________________________________________________________________ block_12_add (Add) (None, 10, 10, 96) 0 block_11_add[0][0] block_12_project_BN[0][0] __________________________________________________________________________________________________ block_13_expand (Conv2D) (None, 10, 10, 576) 55296 block_12_add[0][0] __________________________________________________________________________________________________ block_13_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_13_expand[0][0] __________________________________________________________________________________________________ block_13_expand_relu (ReLU) (None, 10, 10, 576) 0 block_13_expand_BN[0][0] __________________________________________________________________________________________________ block_13_pad (ZeroPadding2D) (None, 11, 11, 576) 0 block_13_expand_relu[0][0] __________________________________________________________________________________________________ block_13_depthwise (DepthwiseCo (None, 5, 5, 576)   5184       block_13_pad[0][0] __________________________________________________________________________________________________ block_13_depthwise_BN (BatchNor (None, 5, 5, 576)   2304     block_13_depthwise[0][0] __________________________________________________________________________________________________ block_13_depthwise_relu (ReLU) (None, 5, 5, 576)   0        block_13_depthwise_BN[0][0] __________________________________________________________________________________________________ block_13_project (Conv2D) (None, 5, 5, 160)        92160   block_13_depthwise_relu[0][0] __________________________________________________________________________________________________ block_13_project_BN (BatchNorma (None, 5, 5, 160)   640      block_13_project[0][0] __________________________________________________________________________________________________ block_14_expand (Conv2D) (None, 5, 5, 960)     153600       block_13_project_BN[0][0] __________________________________________________________________________________________________ block_14_expand_BN (BatchNormal (None, 5, 5, 960)   3840     block_14_expand[0][0] __________________________________________________________________________________________________ block_14_expand_relu (ReLU) (None, 5, 5, 960)    0          block_14_expand_BN[0][0] __________________________________________________________________________________________________ block_14_depthwise (DepthwiseCo (None, 5, 5, 960)  8640    block_14_expand_relu[0][0] __________________________________________________________________________________________________ block_14_depthwise_BN (BatchNor (None, 5, 5, 960)  3840    block_14_depthwise[0][0] __________________________________________________________________________________________________ block_14_depthwise_relu (ReLU) (None, 5, 5, 960)   0          block_14_depthwise_BN[0][0] __________________________________________________________________________________________________ block_14_project (Conv2D) (None, 5, 5, 160)     153600       block_14_depthwise_relu[0][0] __________________________________________________________________________________________________ block_14_project_BN (BatchNorma (None, 5, 5, 160)   640    block_14_project[0][0] __________________________________________________________________________________________________ block_14_add (Add) (None, 5, 5, 160)         0             block_13_project_BN[0][0] block_14_project_BN[0][0] __________________________________________________________________________________________________ block_15_expand (Conv2D) (None, 5, 5, 960)     153600        block_14_add[0][0] __________________________________________________________________________________________________ block_15_expand_BN (BatchNormal (None, 5, 5, 960)   3840      block_15_expand[0][0] __________________________________________________________________________________________________ block_15_expand_relu (ReLU) (None, 5, 5, 960)   0       block_15_expand_BN[0][0] __________________________________________________________________________________________________ block_15_depthwise (DepthwiseCo (None, 5, 5, 960)   8640      block_15_expand_relu[0][0] __________________________________________________________________________________________________ block_15_depthwise_BN (BatchNor (None, 5, 5, 960)   3840      block_15_depthwise[0][0] __________________________________________________________________________________________________ block_15_depthwise_relu (ReLU) (None, 5, 5, 960)    0       block_15_depthwise_BN[0][0] __________________________________________________________________________________________________ block_15_project (Conv2D) (None, 5, 5, 160)    153600     block_15_depthwise_relu[0][0] __________________________________________________________________________________________________ block_15_project_BN (BatchNorma (None, 5, 5, 160)   640      block_15_project[0][0] __________________________________________________________________________________________________ block_15_add (Add) (None, 5, 5, 160) 0 block_14_add[0][0] block_15_project_BN[0][0] __________________________________________________________________________________________________ block_16_expand (Conv2D) (None, 5, 5, 960)   153600     block_15_add[0][0] __________________________________________________________________________________________________ block_16_expand_BN (BatchNormal (None, 5, 5, 960)   3840     block_16_expand[0][0] __________________________________________________________________________________________________ block_16_expand_relu (ReLU) (None, 5, 5, 960)    0      block_16_expand_BN[0][0] __________________________________________________________________________________________________ block_16_depthwise (DepthwiseCo (None, 5, 5, 960)   8640       block_16_expand_relu[0][0] __________________________________________________________________________________________________ block_16_depthwise_BN (BatchNor (None, 5, 5, 960)   3840     block_16_depthwise[0][0] __________________________________________________________________________________________________ block_16_depthwise_relu (ReLU) (None, 5, 5, 960)    0   block_16_depthwise_BN[0][0] __________________________________________________________________________________________________ block_16_project (Conv2D) (None, 5, 5, 320)         307200        block_16_depthwise_relu[0][0] __________________________________________________________________________________________________ block_16_project_BN (BatchNorma (None, 5, 5, 320)         1280        block_16_project[0][0] __________________________________________________________________________________________________ Conv_1 (Conv2D) (None, 5, 5, 1280)           409600           block_16_project_BN[0][0] __________________________________________________________________________________________________ Conv_1_bn (BatchNormalization) (None, 5, 5, 1280)      5120          Conv_1[0][0] __________________________________________________________________________________________________ out_relu (ReLU)        (None, 5, 5, 1280)        0            Conv_1_bn[0][0] ================================================================================================== Total params: 2,257,984 Trainable params: 0 Non-trainable params: 2,257,984 _________________________________________________________________________ Explanation

The convolutional base created from the previous step is frozen and used as a feature extractor.

A classifier is added on top of it to train the top-level classifier.

Freezing is done by setting layer.trainable = False.

This step avoids the weights in a layer from getting updated during training.

MobileNet V2 has many layers, hence setting the model’s entire trainable flag to False would freeze all the layers.

When layer.trainable = False, the BatchNormalization layer runs in inference mode, and won’t update mean and variance statistics.

When a model is unfreezed, it contains BatchNormalization layer to do fine-tuning.

This can be done by passing training = False when the base model is called.

Else, the updates applied to non-trainable weights will spoil what the model has learned.

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You can get your hands on the Asus Vivo Tab TF600 for $600 featuring a 10.1-inch IPS display with 1280-by-800 resolution, quad-core Nvidia Tegra 3 1.3GHz processor, 32GB storage, 2GB RAM, Webcam, 8 megapixel rear-facing camera, Bluetooth 4.0, 802.11b/g/n Wi-Fi, SDHC card reader, and Windows RT. The TF600 weights 1.1 pounds and is 0.3-inches deep. You can also pre-order the Vivo Tab’s keyboard dock for $170.

Ultrabook fans can pre-order the $850 HP Envy Ultrabook 4-1130us featuring a 14-inch display, 1.7GHz Core i5, 6GB DDR3 RAM, 500GB HDD, 32GB SSD, 2 x USB 3.0, 1 x USB 2.0, HDMI out, and 802.11b/g/n Wi-Fi. HP’s Envy Ultrabook weighs 3.86 pounds and measure 0.78-inches thick.

Another much-discussed device available at Staples is the 11.6-inch Samsung Series 5 slate for $650. This dockable tablet features 1366-by-768 screen resolution, 1.5GHz Atom Z2760 ( Clover Trail) processor, 64GB SSD, 2GB DDR3 RAM, 802.11a/b/g/n Wi-Fi, Bluetooth 4.0, micro HDMI, USB 2.0, and 64-bit Windows 8. The keyboard dock for the Series 5 is sold separately, but Staples was not offering it at the time of this writing.

Tiger Direct is selling a number of HP Probook laptops ranging in price from $600 to $680 featuring 14- or 15.6-inch screen sizes. You can also check out the Home Shopping Network’s selection of Acer, Gateway, and HP computers, many of which were discussed earlier in the week.

Finally, Dell is offering several Windows 8 devices for pre-sale Friday including the XPS 12, XPS 13, XPS One 27, and Inspiron One 23. The XPS 12 Convertible Ultrabook is one of the most talked about Windows 8 devices, because of the unique flip-hinge design that lets you turn the screen a full 180 degrees to convert the laptop into a tablet. Pricing for the XPS 12 starts at $1200 featuring a 12.5-inch touch screen, 1.7Ghz Intel Core i5 processor, 4GB DDR32 RAM, and 128GB SSD.

Windows 8 pre-sales come after the Home Shopping Network on Monday jumped the gun and mistakenly started early sales of Acer and Gateway Windows 8 devices.

How Debugging Works In Tensorflow?

Introduction to TensorFlow Debugging

In this article, we will try and understand what the different ways of debugging can be done in TensorFlow. Generally debugging is very useful for finding out the values getting flown down in the code and where exactly the code is breaking. All the languages present in the market provide inbuilt functionality for debugging. Similarly, in TensorFlow also provides different classes and packages with which we can identify the flow of the data in the algorithms and optimize the algorithm’s performance.

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How Debugging Works in TensorFlow?

Now let’s see how the debugging works in TensorFlow.

The core program part where the debugging can be enabled in TensorFlow are:

graph(though the use of this function we can build a computation graph)

session(though the use of this function we can execute the graph)

there are in total 4 ways as shown below through which we can perform debugging in TensorFlow

1. Fetching and Printing Values for a Particular Tensor

This is the easiest to use step where we can add breakpoints and print out the values to get the required information

Advantage:

It is very easy and quick to implement.

And information can be fetched from anywhere we want.

If we print any information at any point, then that will create a reference to that particular tensor which is not a good practice to keep

2. The tf.print function

This method can come handy while checking some output in runtime. It will just create a log for the particular line in the with the use of the session.run() method.

Advantage:

This method is handy as it helps us to monitor the development of the values during the run time.

Since this creates a log of the terminal data during the execution of the algorithm, it might fill up the screen with the logs that are not a good practice Afterall.

just want to discuss the tool which TensorFlow provides called Tensor Board. It’s a web UI for TensorFlow visualization developed by Google and runs locally in the system. Below is the screenshot for the website. It is generally used to visualize the performance of the TensorFlow algorithm and monitor its performance. This Dashboard also comes with a plugin for debugging.

3. TensorBoard visualization

With this visualization, we can use to monitor various things about the out model, such as:

We can summarize the model.

View the performance.

Serialize the data in the model.

Clean the graph and give proper nomenclature.

This is basically more or less a monitoring tool used to monitor the performance of our model.

Now moving on to TensorBoard Debugger.

4. TensorBoard Debugger

As explained earlier, TensorBoard is a visualizing tool so that visualization can be debugged using this plugin. It provides various cool debugging features such as:

We can select particular nodes in the Tensor and debug them.

Graphically we can control the execution of the model.

And finally, we can also visualize the tensors and their values.

Below is the screenshot of this TensorBoard Debugger in action:

The code TensorFlow packages which are used for the debugging are:

Here tf_debug is the debugger that needs to be imported from the TensorFlow.python package to run the debugging on TensorFlow.

And the below two lines are used to invoke the TenorBoard locally through the terminal.

Advantages of TensorFlow Debugging

We can identify we can output value and a particular stage through the use of debugging while the algorithm is getting trained.

Using the Tensor board application, we can identify and see the performance of our algorithm in a graphical format.

We can also run the execution of each and every step of our model using the GUI provided in the Tensor Board.

The TensorBoard application is very user friendly and easy to understand.

With the use of a debugger or rather Tensor Board, we can identify if we still need more data cleaning is required on our training data.

Conclusion

In this article, we learned about the debugging in TensorFlow, the packages present for TensorFlow’s debugging purpose, and how to implement them. We have also seen the use of tensor board applications, which is a useful tool to debug the algorithm while getting trained.

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Learn What Is Tensorflow Concatenate?

Introduction to tensorflow concatenate

Tensorflow concatenate is the methodology using which we can join and make one resultant out of two values. Concat() function is used in tensorflow framework for concatenating the tensprs along with 1-d format. In this article, we will have to try to gain knowledge about What is tensorflow concatenate, How to Concatenate, how to use tensorflow concatenate, tensorflow concatenate feature, tensorflow concatenate example and finally conclude our statement.

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What is tensorflow concatenate?

tensorFlowObject.Concat(input values, axis, operation name)

The parameters mentioned in the above syntax are as described one by one below –

Input values – This is the source input tensor or the list of tensors that we want to concatenate.

Axis – It is a tensor value of zero dimensions and helps in specifying the dimensions that needed to be followed while concatenating.

Operation name – It is an optional argument that needs to be passed in order to define the name of the operation to be performed.

Return value – The output of the concat() function is the tensor that has the concatenated value of the supplied input tensor arguments.

The concatenate layer is responsible for joining the input values and inherits its functionality from Module and Layer classes. Instead of concat() function, we can also make use of the function –

tensorflow.keras.layers.Concatenate(axis = 1, ** keyword arguments of standard layer)

Also, note that the shape of all the input tensors that are being supplied should be the same. The exceptional value in this is the axis of concatenation. As previously mentioned, in case of concatenate() function as well only one output tensor containing the combined or joined input tensors is obtained.

How to Concatenate?

One can simply concatenate the two or more tensor values stored in two or more different variables or objects by passing the list of them enclosed in square brackets [tensor1, tensor2, …] like this to input as the second parameter and first parameter with the value of the axis specifying the dimensions of the tensor to the concat() function. For example, if we have two matrices/tensors that have the same shape –

[[ 21,  22,  23], [ 24,  25,  26]]

[[ 21, 22, 23], [ 24, 25, 26]]

AND

[[ 27, 28,  29], [30, 31, 32]]

[[ 27, 28, 29], [30, 31, 32]]

After we make the use of concat() tensorflow function to concatenate both of them, our matrix will look as shown in the below tensor value –

[ 24,  25,  26]], [[ 27, 28,  29], [30, 31, 32]]]

Used tensorflow concatenate

[[[ 21, 22, 23],[ 24, 25, 26]], [[ 27, 28, 29], [30, 31, 32]]]

The tensorkflow concatenate function can only be used provided if you have two or more tensor values of the same shape that you want to concatenate. Note that if the shapes of matrices are not the same then you will need to reshape the vectors before manipulating it for the concatenate() function.

The first step that we will follow once we have our inputs ready with us in the program is to import the necessary libraries and packages in the beginning.

Prepare the input tensors. Store them in objects or make a list of the same and pass it as the argument. If the shape is not the same then reshape before passing it as input.

Pass the axis and the input arguments to the tensor matrix.

tensorflow concatenate feature

The feature or properties of concatenate in tensorflow are as mentioned below –

Activity regularizer – This is an optional function and is used for preparing the output of this concatenation layer in tensorflow.

Dt type input – This is used for retrieval of the layer input and is only applicable if we have only single input for the layer. It then returns an output consisting of a list of tensors or a single tensor.

Losses – This loss is actually associated with the layer of concatenation. The tensors responsible for regularizing the tensors are also generated and created by using the property of loss associated and accesses by the layer. The working is similar to the eager safe which means the access of losses will propagate gradients under a tensorflow.Gradient back to the variables associated with it.

Non- trainable weights

Non- trainable variables

Output_mask – This property or feature is only applicable if the concatenation layer consists of only a single inbound node that means if the connection of only a single incoming layer is created. This feature helps in retrieving the output massk tensor of the layer.

Output_shape – Applicable only if one output layer is present or the shape of all the outputs has the same shape.

Trainableweights

Trainable variables

Set weights

Get weights

Get updates for

Get output shape at

Get output mask at

Get output at

Get losses for

Get input shape at

Get input mask at

Get input at

Get config

From config

Count params

Compute output shape

Compute mask

build

tensorflow concatenate example

Here are the following examples mention below

Example #1

Code:

The output of executing the above program is as shown in the below image –

Example  #2

Code:

return educbaModel

The output of executing the above program is as shown in the below image –

Conclusion

The tensorflow concatenate function is used in tensorflow to combine or join two or more source tensors and form a single output consisting of a single tensor that has both the input tensors combined in it. We can provide and specify the input axis dimension which helps in representing the dimensions of tensor.

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This is a guide to tensorflow concatenate. Here we discuss What is tensorflow concatenate, How to Concatenate, how to use tensorflow concatenate. You may also have a look at the following articles to learn more –

Top 5 Important Models Of Tensorflow

Introduction to TensorFlow Models

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Various TensorFlow Models

The Heart of Everyday technology today is a neural network. These neural networks inspire deep Learning Models.

Neural Networks are like neurons in the human brain; these neurons have the capability to solve complex problems. These simple neurons are interconnected to each other to form a Layered Neural Network. This Layered neural network contains Input Layers, Output layers, Hidden Layers, Nodes, and Weights. The input layer is the first layer from where the input is given. The last layer is the output layer. The Hidden layers are the middle layers that carry out processing with the help of nodes (Neurons/operations) and weights (signal strength).

Various models of TensorFlow are:

1. Convolutional Neural Network

Layers in CNN:

Convolution layer: It is a Layer where we convolve the data or image using filters or kernels. These Filters we apply to the data through the sliding window. The depth of the filter is the same as the input. For a colour image, RGB values give the filter of depth 3. It involves taking the element-wise product of the filters’ image and then summing those values for every sliding action. The output of a convolution of a 3d filter with a colour image is a 2d matrix.

Activation Layer: The activations functions are between convolutional layers that receive an input signal, perform non-linear transformations and send the transformed signal to the next input layer of neurons. Different activations functions are sigmoid, tanh, Relu, Maxout, Leaky ReLU, ELU. The most widely used activation function is Relu. Non-linear transformations are used to make a network capable of learning and performing complex tasks.

Pooling Layer: This layer is responsible for reducing the number of parameters and complex computation in the network. At pooling, Average pooling and Max pooling are performed.

Fully Connected Layer: It connects every neuron to every previous neuron. It is the output layer on CNN. This is the last phase of CNN. CNN should be used when the input data is the image, 2d data can be converted to 1d, and when the model requires a great computation amount.

2. Recurrent Neural Network

A recurrent Neural network (RNN) is a network of at least one feedback connection forming a loop. RNN is powerful as to retain information for some time, to do temporal processing and learning sequences. RNN retain information means it store information about the past, which helps learn the sequence. The RNN can be – Simple RNN with at least one feedback connection or a fully connected RNN. One example of RNN is Text generation. The model will be trained with lots of words or with some author’s book. The model will then predict the next character(o) of the word(format). The auto prediction, which is now available in emails or smartphones, is a good example of RNN. RNN is invented for predicting sequences. RNN is helpful for video classification, Sentiment analysis, character generation, Image captioning, etc.

3. LSTM

LSTM (Long short term memory) is one of the most efficient problems for sequence prediction. RNN is quite effective when dealing with short term dependencies. RNN failed to remember the context and something which is said long before. LSTM networks are very good at holding long term dependencies/memories. LSTM is useful for handwriting recognition, handwriting generation, Music generation, image captioning, language translation.

4. Restricted Boltzman Machine

It is an undirected graphical model and has a major role in deep learning frameworks like TensorFlow. It is an algorithm used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modelling. In RBM, there are visible layers and Hidden layers. The first layer of RBM is the visible or input layer. The nodes perform calculations and are connected across layers, but no two nodes of the same layer are linked, so there is no internode communication restriction in RBM. Each node processes input and makes a stochastic decision on whether to transmit the input or not.

5. Autoencoders

Encoder: Takes input image and compresses it, and produces code.

Decoder: It reconstructs the original image from the code.

Autoencoders are Data specific; it means that they can compress images only on which it is trained. The Autoencoder, which is trained to compress images for cats, would not compress images of humans.

6. Self-Organizing Maps

Self-Organizing maps are helpful for feature reduction. They are used to map high dimensional data to lower dimensions which provide good visualization of the data. It consists of an input layer, weights, and Kohonen layers. Kohonen layer is also called a feature map or competitive layer. The self-organizing map is good for data visualization, dimensionality reduction, NLP, etc.

Conclusion

TensorFlow has huge capabilities to train different models with more great efficiency.  In this article, we studied different deep learning models which can be trained on the TensorFlow framework. We hope that you have gained insight into some of the deep learning models.

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This is a guide to TensorFlow Models. Here we discuss the introduction to TensorFlow model along with five different models explained in detail. You can also go through our other suggested articles to learn more–

This Week’s Top Stories: Apple Silicon Event, Homepod Mini And Iphone 12 Pre

In this week’s top stories: Apple announces Apple Silicon event for November 10, new AirPods 3 images, HomePod mini and more iPhone 12 pre-orders. Read on for all of this week’s top stories.

November 10 event

Apple this week officially announced its third event in as many months. The event will take place on November 10 at 10 a.m. PT, and Apple is teasing the event with the hashtag “One more thing.”

Apple’s November event will be live-streamed across Apple’s website, in the Apple TV app, and on YouTube. It’s completely remote and virtual due to the ongoing COVID-19 pandemic.

Apple Silicon details

This November event is expected to focus on the upcoming transition to Apple Silicon in the Mac lineup, with Apple having promised its first Apple Silicon machine would come before the end of the year.

A Bloomberg report this week indicated that Apple will announce at least two new Apple Silicon Macs, including a new 13-inch MacBook Pro and a new MacBook Air. Apple is also said to be increasing production of a new 16-inch MacBook Pro with Apple Silicon, but it’s unclear whether this machine will be announced this year.

Looking ahead to the rest of the Mac lineup, Bloomberg also added that Apple is developing a new Mac Pro that’s roughly have the size of the current model, as well as a new Mac mini and a new iMac, all of which will feature Apple Silicon.

AirPods 3 leak

Rumors of new AirPods have gained traction this week. Newly leaked images on Wednesday claimed to offer our first look at AirPods 3 with an AirPods Pro-inspired design. This comes after a separate report last month indicated that Apple is working on new AirPods and AirPods Pro for 2023.

You can get a look at the leaked AirPods 3 images right here.

Spotify for Apple Watch

Spotify officially started rolling out support for streaming music directly on Apple Watch this week. This comes two years after Spotify released the first version of its Apple Watch, and two months after Spotify started testing the feature with a very small subset of users.

The feature is available to many Apple Watch users now, but Spotify has cautioned that it’s still a beta and that it may take some time for the new standalone streaming option to be available to all Apple Watch owners..

iPhone 12 mini, iPhone 12 Pro Max, and HomePod mini

Rounding out this week, Apple has opened pre-orders for the iPhone 12 mini, the iPhone 12 Pro Max, and the HomePod mini.

As a refresher, the iPhone 12 mini features a 5.4-inch display with a feature set that is nearly identical to the 6.1-inch iPhone 12. The iPhone 12 Pro Max is the largest iPhone ever made, packing a 6.7-inch display alongside a triple-lens camera array with a larger sensor, a LiDAR scanner, and more.

The iPhone 12 mini starts at $699, while the iPhone 12 Pro Max starts at $1099. The HomePod mini goes for $99.

These and the rest of this week’s top stories below.

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Listen to a recap of the top stories of the day from 9to5Mac. 9to5Mac Daily is available on iTunes and Apple’s Podcasts app, Stitcher, TuneIn, Google Play, or through our dedicated RSS feed for Overcast and other podcast players.

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Jeff Benjamin joins Zac Hall to give Apple Watch Series 3 and Series 5 an exit interview before new models are announced. 9to5Mac Watch Time is a podcast series hosted by Zac Hall. In this series, we talk to real people about how Apple Watch is affecting their lives.

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Week 6

9to5Mac Watch Time

This week on Watch Time join 9to5Mac’s Zac Hall and Tempo developer Rahul Matta in the final episode of this podcast “season”.

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Toning down the Apple Watch: Tips and feature requests to avoid being overwhelmed Apple releases watchOS 7.4.1 with security improvements Apple Watch blood sugar and blood pressure measurement could be a step closer watchOS 7.4 brings iPhone mask unlock feature for Apple Watch

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Week 6

46:48

With guest Sigmund Judge

42:59

Week 5

52:45

Week 4

57:20

Week 3

56:40

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