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Windows 8 PC sales start Friday with major online retailers including Best Buy, Dell, Staples, Tiger Direct, and yes, the Home Shopping Network taking pre-orders for Windows 8 PCs and tablets. Some retailers are promising free shipping and delivery on October 26, also known as Windows 8 launch day. It’s not clear if Microsoft is allowing select partners to offer Windows 8 PCs on Friday or if all computer resellers will start to roll out Windows 8 pre-orders in the coming days. Major online store fronts such as Amazon and Walmart were not yet offering Windows 8 devices at the time of this writing.
If you want to be one of the first on your block with a PC built for Windows 8, here’s a quick look at some of the highlights offered online.
If you pre-order a Windows 8 PC from Best Buy before noon Central/1 Eastern on Wednesday, October 24, Best Buy will give you free shipping on your offer and deliver your item on October 26. You can order items such as the Hewlett-Packard Envy h8-1414 desktop PC for $700 with 3.5GHz AMD FX processor, 10GB DDR3 RAM, 1 TB hard drive, AMD Radeon HD 7450 graphics with 1GB dedicated memory, Ethernet, 802.11b/g/n Wi-Fi, DVD drive, and 2 x USB 3.0. HP in late September announced several new Windows 8 PCs including the upcoming HP Envy Phoenix h9 desktop.
You can also pick-up the recently announced Acer M5-581T Ultrabook available exclusively at Best Buy. The laptop features a 15.6-inch screen with 1366-by-768 resolution, 1.7GHz Intel Ivy Bridge Core i5 processor, 6GB RAM, 520GB HDD, 802.11b/g/n Wi-Fi, 2 x USB 3.0 and one USB 2.0, HDMI out, and 64-bit Windows 8. The Ultrabook weighs 5.1 pounds and is priced at $600. Best Buy was not yet selling the M5-481PT, a 14-inch Ultrabook featuring a 10-point multi-touch display.
Best Buy is also offering the Lenovo IdeaPad Yoga 13 for $1,000 featuring a 13.3-inch screen with 1600-by-900 resolution, 1.7 GHz Intel Ivy Bride Core i5 processor, 4GB DDR3 RAM, 128GB SSD, 802.11b/g/n Wi-Fi, one each of USB 3.0 and 2.0 ports, and 64-bit Windows 8.
You can also get Windows 8 devices from Asus, Dell, Gateway HP, Toshiba, Samsung, and Sony at Best Buy.
StaplesIf you pre-order a Windows 8 PC or tablet with Staplesbefore October 25, the office supplies chain will deliver your new device between October 26 and October 31. Staples is not promising free shipping or guaranteed October 26 delivery. But anyone who orders a Windows 8 PC from Staples priced at $699 and above will receive free data transfer service from their old computer.
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.
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How Can Tensorflow And Pre
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.
Getac V200 Rugged Core I7 Tablet Pc Outed
Getac V200 rugged Core i7 tablet PC outed
Rugged computer specialist Getac has outed its successor a sibling to the V100 convertible tablet PC, and as you might expect it’s no less sturdy. The Getac V200 has a 12.1-inch super-bright 1,200 nit multitouch-capable display, 2.0GHz Intel Core i7-620LM processor and WiFi b/g/n, but more importantly perhaps it’s MIL-STD-810G and IP65 compliant for resilience to dust, vibration, temperatures as low as -20 degrees centigrade and water.
Other specs include gigabit ethernet, Bluetooth 2.1, USB, VGA, RS232, eSATA, an SD card reader and PCMCIA, while GPS and 3G are optional. Buyers can optionally swap out the standard 320GB hard-drive for an 80GB SSD, too. Data security is taken care of with a smartcard reader, fingerprint scanner and hardware encryption, and you can swap the battery without having to completely shut down the tablet.
The touchscreen is resistive, which was an intentional decision: that means gloved fingers can use it, handy when you’re in icy conditions. We’re waiting to hear back on pricing.
Update: Getac tell us the V200 is priced between $3,799 and $5,099 depending on screen size and options ordered.Update 2: Getac also says the V100 will remain on sale alongside the V200, offering users a choice of screen sizes.
Press Release:
GETAC INTRODUCES WORLD’S MOST POWERFUL FULLY RUGGED CONVERTIBLE POWERED BY AN INTEL CORE i7 2.0 GHZ PROCESSOR
Intel® Core™ i7 2.0 GHz Processor, 1200 NITs QuadraClear™ Display,
Glove-Friendly Multi-Touch LCD, Full-Size 88-Key Keyboard, LifeSupport™ Battery Swapping System Option and 5-Year Bumper-to-Bumper Warranty
IRVINE, CA. September 07, 2010– Getac, a leading innovator and manufacturer of rugged computers that meet the demands of field-based applications, is introducing its new V200 rugged convertible, the world’s most powerful fully rugged convertible notebook*. The V200 also offers more features and functions than previous models, making it one of the most useful all-purpose rugged convertibles on the market.
Perfectly suited for vehicle mounting or field use, the notebook-to-tablet V200 convertible features a 12.1-inch wide screen display and comes standard with Getac’s exclusive 1200 NITs QuadraClear™ technology, enabling users to easily read and operate their computer under direct sunlight. In addition, the display offers a resistive multi-touch capability enabling workers to manipulate the screen by a series of single and multi-finger gestures even while wearing gloves—a necessity when working in extreme weather conditions or dangerous environments.
“The new V200 is the most powerful and feature rich fully rugged convertible available,” explains Jim Rimay, president, Getac. “Its rugged design, fast processor and extensive features and functions make it the most useful, all-purpose tool for field-based applications in the industry.”
Weighing under 6 pounds, the V200 is MIL-STD-810G and IP65 compliant, adding to its rugged design and flawless performance in harsh environments. Along with its full magnesium alloy casing, the fully-rugged convertible comes standard with low-temperature operation allowing the computer to be used in -20C environments. The V200 also includes a shock mounted 320GB hard drive and sealed I/O caps and doors to prevent damage from solid particles and moisture. Users can also configure the V200 with an 80GB solid state drive.
A built-in security feature using Intel® vPro™ technology enables IT specialists to maximize hardware-assisted security to better maintain, manage, and protect their business PCs. The V200 has a conveniently located finger print reader on the display face for the security required by government and corporate data management. Configured with a built-in Smart Card reader, the V200 increases identification & authentication security on administrator and remote access accounts while security criteria EAL 5+ encrypts valuable data by hardware, making data transfer more secure.
A groundbreaking new option on the V200 is Getac’s innovative LifeSupport™ battery swapping system. The LifeSupport™ battery swapping system allows field users to quickly change out a depleted battery with a fresh charged one. By entering standby mode, the user has approximately two minutes to replace the battery without shutting the computer down or closing applications.
As with all of Getac’s fully rugged notebook, tablet and convertible computers, the V200 is backed by the industry’s best 5-year bumper-to-bumper warranty. Enjoy the peace of mind that comes with knowing that the V200 is covered by a warranty that includes damage that occurs due to accidental acts and environmental exposure. The new V200 is will be available at the beginning of October through Getac Authorized Dealers.
About Getac
* Testing conducted by Getac in July 2010. Performance tests are conducted using specific computer systems and reflect the approximate performance of the V200 versus competitive computers current at the time of testing.
Microsoft Thinks Byod With Surface 2 Tablet And Windows Rt 8.1
Though consumers are a big target audience for the new Surface 2 tablet, Microsoft is also hoping to woo businesses with features that could make the device easier to secure and manage in IT environments.
The software maker introduced new Surface tablets this week—the Surface 2 with Windows 8.1 RT and Surface Pro 2 with Windows 8.1. The new Surface 2 features that could be attractive to business users include mobile-device management and virtual private network (VPN) support, said Cyril Belikoff, director of Surface, during an interview at the tablet launch event.
The Surface 2 tablet also has remote lockdown and stronger security features compared to the earlier Surface RT, Belikoff said, adding that the device will be easier for IT administrators to manage.
The tablet’s starting price is $449, but enterprises buying Surface 2 in bulk could get a discount, Belikoff said. Belikoff also showed a charging cabinet—targeted at hospitals and schools—in which multiple Surface tablets could be charged simultaneously.
It’s true that the number of business features in the Surface 2 may not match those of the Surface Pro 2, which was also announced on Monday. The Surface Pro 2, which starts at $899, has a host of multimedia and connectivity options that may make it a better fit in enterprises. For example, a Surface Pro 2 docking station provides more network and display connectivity, features that are not available on Surface 2. However, the Surface 2 now has a USB 3.0 port, which is an improvement from the USB 2.0 port in its predecessor.
The Surface Pro 2, introduced this week, may appeal to businesses with Bring Your Own Device policies for hardware.
Also, the Surface Pro 2—which Microsoft officials called a PC replacement—will run applications that previously ran on Windows 7 because the tablet is powered by Intel’s fourth-generation x86 Core i5 processor, code-named Haswell. The Surface 2 runs on an ARM processor, which is widely used in mobile devices but does not support older Windows x86 applications.
Tablets are increasingly being seen as alternatives to PCs, so it makes sense for Microsoft to target the enterprise market with Surface 2, said Roger Kay, principal analyst at Endpoint Technologies Associates.
IT administrators are usually open to bringing in personal tablets and smartphones with different OSes, and Surface 2 could easily fit into existing environments that rely on Microsoft software, Kay said.
“When you start structuring in communications and security, it will likely appeal to IT managers to put the tablet into the field,” Kay said.
The Surface 2 may also appeal to business users as a lighter-weight, lower-cost device than Surface Pro 2, said Jack Gold, principal analyst at J. Gold Associates.
“There will be a few that will buy it because they want to save bucks over the Pro,” Gold said.
Microsoft made a smart decision to package Surface tablets with Microsoft Office and Outlook software, which are widely used by individuals and businesses, Gold said.
“You can’t do Outlook on Android and iOS,” Gold said.
Dell has targeted its XPS 10 tablet with Windows RT to enterprises, but found few buyers. The tablet, which was priced starting at $499 at the time of its launch last year, was considered too expensive for consumers, especially for a device that did not run the gamut of Microsoft applications. Microsoft took a $900 million charge resulting from slow sales of the Surface RT device. Lenovo, Asus and Samsung stopped selling tablets with Windows RT after poor sales.
Another challenge could be to get users to accept the Windows 8.1 RT OS, which is an update to the failed Windows RT. But Microsoft is trying to break off from the legacy Windows software used in PCs and thinking ahead with Windows RT 8.1, which is a highly mobile and modern OS, Kay said.
Microsoft officials are expected to talk about more enterprise features on the Surface tablets next month.
8 Best Windows 10/11 Health And Fitness Apps
8 Best Windows 10/11 Health and Fitness Apps
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Do you want to live a healthier life? Of course you do, as this has become everybody’s goal since it is so hard to find healthy meals and also since we all have such a busy schedule. Nowadays it’s really difficult to actually take a break, or to relax after having a busy day at work. At the end of the day, we are tired and we just want to prepare ourselves for the next day, which will basically be the same. So, when do we make something just for us? Or when do we have time to live a healthier life? I know that it is difficult even to give an actual answer to these questions, so because of that during the lines from below I will try to help you out.
How? Well, basically I will describe you the best Windows 8 health and fitness apps. In a few words, these Windows 8 apps can be downloaded and installed on your smartphone and tablet which means that you can stay in touch with your health and fitness program even when you are at work. Each time you have a break you can access the app and resume your “health or fitness program”. Of course, in order to do that you will have to own a Windows 8 based smartphone or tablet and also you have to be confident on the results.
Keep fit with these health and fitness apps for Windows 8The apps presented here will be:
Back Trainer
Endomondo
FitBit
Running Mate
Nutrient’s
Diet and Weight Control
Yoga
Daily Workouts
1. BackTrainerWe have already described you the BackTrainer Windows 8 fitness app (read the review by going here) during our recent posts. As you might know, BackTrainer is a dedicated app that can help you against your back pain. The program will teach you the right exercises that will take away the back pain, so after using this Windows 8 fitness app you will feel better and you will be able to have a normal life without dealing with stressful pains. You can anytime download BackTrainer from here, for only $2.49. You can also get the app for free for one day only – there is a one day free trial version available.
2. Endomondo 3. FitBitFirst, this Windows 8 fitness and health app is designed to be used with Fitbit devices. The app will let you sync your Fitbit tracker with the help of the wireless sync USB dongle. Now, with Fitbit you can view the results of your activity exercises along with a proper view of your health and fitness trends. You can schedule new activities, you can see the results you have obtained and you can learn how to lose weight without taking pills and how to live a healthier life. You can read our Fitbit review as soon as you access the link from here.
Expert tip:
4. Running MateThis Windows 8 app uses a GPS connection in order to help you track your fitness workouts. Running Mate can be used for cycling, walking and running exercises; it is accurate and helps in improving your results. The app also tells you when you are skipping your workout and it motivates you in achieving new levels on your fitness program. Running Mate is priced $1.99 and can be anytime downloaded from Windows Store on your Windows 8 based tablet or smartphone.
5. Nutrient’sThis is a free Windows 8 health app that can help in you choosing the right alimentation. Along with a fitness program, Nutrient’s will teach you how to lose weight fast and how to feel better without eating meat or bread. Basically the app describes the nutrients that are present in different fruits and shows you how and when to eat fruits for getting the energy needed for your organism.
6. Diet and Weight ControlEveryone should have this Windows 8 health app on their smartphone or tablet. This is the best app to use when having weight problems or when trying to lose weight fast. So, Diet and Weight Control will tell you which diet to take in order to reduce your weight. You will learn how to eat properly and of course what to eat if you want to be relaxed and confident about yourself. This Windows 8 app can be downloaded for free from Windows store.
7. YogaNow, this is the best Windows 8 app to use if you want to forget about your daily problems. Yoga will teach you how to be relaxed and how to enjoy your life. You will be able to learn more than 24 types of yogas (along with step by step process for each yoga exercise), exercises which can be performed right from your home. The tool has a user friendly interface and can be installed on Windows 8.1 OS. You can download Yoga from Windows store for free. As an extra app, you can also check out GymGuide app for Windows 8 that we have featured a while ago.
8. Daily WorkoutsThis is a great app that will be your personal trainer anywhere you will go. It is designed for all users, even for the laziest as it offers you daily exercises sets from 5 to 10 minutes. You don’t have to spend hours in order to learn some sophisticated exercises. You will have to choose between more than 170 exercises for all the major muscles and it is designed to give programs for both women and men.
If you struggle to learn some specific workout exercises, don’t worry: the app comes with videos for all exercises and explanations for the right technique. If these 170 exercises are not enough for you, you can purchase more. There are also some prepared routines that you can purchase and follow them in order to keep your body in a good shape.
Download now Daily Workouts free from Microsoft Store
If you are interested in more fitness and workout apps that could help you exercising daily, you can check our list with best apps for fitness. In case you are looking for some apps that can guide you for a more ‘bodybuilder program’ check these apps.
Read Also: 2014 Round-up: Best Windows 8 Tablets
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Best Apps For Freelancers And Professionals For Windows Pc
As a freelancer, I spent most of my time on the computer. While freelancing offers freedom, I depended more on my computer for growth, entertainment, and interaction. Thus, I thought of compiling this list of apps for Freelancers on Windows 10. As a freelancer, you might need Windows Store apps that could save you time with petty jobs as well as those which could help keep you in the right mood while working alone.
Windows apps for Freelancers & ProfessionalsThis post details some of the best apps available for Freelancers and Professionals for Windows 11/10 PC on the Microsoft Store.
App Installer
OneDrive
Tuber
8 Music Cloud
Todoist
Money Lover
Duolingo
Code Writer
The list is as follows:
1] App InstallerYou also have error messages and solutions when there is one. Available for all Windows desktops and PCs. Download it from here.
2] OneDriveThe Windows equivalent of Google Drive but with way more utility and space for you. You can store everything on the cloud, starting from files, photos, and even apps. Now, connect and get done with everything, no matter where you are.
Powering your mobility of sharing everywhere. Get it from here.
3] TuberThis one is for all the video and audio hoarders out there who would love a collection that is all to themselves. Download any material from YouTube 4K (2160p) QHD (1440p) Full HD (1080p) SD (480p) and also in various file sizes too.
A very handy app to have if you love watching your playlist over and over again without having to access the internet always. Get it from here for free.
4] 8 Music CloudA very user-centric SoundCloud Player that lets you explore the world of music and discover new favorites every day. Share your favorites and follow all the music artists you love and even the indie artists too.
Download from here for free.
5] TodoistTodoist app boasts the “Application Creator of the Year” Nominee – Windows Developer Awards. Life can get hectic, and such to-do-lists, and task managing apps help you stay on top of things no matter where you are.
Forgetting things is no longer a problem anymore. Just download this app and aligning your tasks to get notifications and lets you finish your lists in no time and be ahead of all your deadlines too. Download it from here.
6] Money LoverThis one is for those money spenders who need speed bump on their expenditures. Track your expenses daily and never go overboard again. Native to Windows 10, you can have it on all your Windows devices as long as you have a decent internet connection.
Download it for free from the Windows Store here.
7] DuolingoWhile many might question this option on the list, learning new languages is helpful for freelancers. It helps you open up to new opportunities. Learning a new language is not easy. But, Duolingo is surely there to help with that. Choose from a wide range of courses on Spanish, French, German, Portuguese, Italian, Irish, Dutch, Danish, and English. You can also have it on the go on your Windows 10 device.
Download it here for free.
8] Code WriterWith more than 200 supported files, you can use this app to write and edit all your codes for your websites. Built from the ground for Windows 10, you can use this very easy app to write for all your desktop apps too. Easy command access and keyboard access too, create and edit form all accessible sources.
Download it here for free.
When Windows Store comes to utility apps, they have outdone themselves. You can find all most all kinds of apps to make your daily life easier and help you stay right on top of every deadline and update. This is where the above list is curated to help your daily functions and find the apps that help you. All are 100% free to use and easy to download too.
Update the detailed information about Windows 8 Pc And Tablet Pre on the Hatcungthantuong.com website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!