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Image Rendering problem resolved #1178

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Feb 20, 2020
Merged

Image Rendering problem resolved #1178

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4 changes: 2 additions & 2 deletions notebooks/chapter19/Learners.ipynb
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Expand Up @@ -318,7 +318,7 @@
"\n",
"By default we use dense networks with two hidden layers, which has the architecture as the following:\n",
"\n",
"<img src=\"images/nn.png\" width=500/>\n",
"<img src=\"images/nn.png\" width=\"500\"/>\n",
"\n",
"In our code, we implemented it as:"
]
Expand Down Expand Up @@ -500,7 +500,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
"version": "3.6.9"
}
},
"nbformat": 4,
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6 changes: 3 additions & 3 deletions notebooks/chapter19/Loss Functions and Layers.ipynb
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Expand Up @@ -40,7 +40,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src= images/mse_plot.png width=500/>"
"<img src=\"images/mse_plot.png\" width=\"500\"/>"
]
},
{
Expand Down Expand Up @@ -88,7 +88,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src= images/corss_entropy_plot.png width=500/>"
"<img src=\"images/corss_entropy_plot.png\" width=\"500\"/>"
]
},
{
Expand Down Expand Up @@ -390,7 +390,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
"version": "3.6.9"
}
},
"nbformat": 4,
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6 changes: 3 additions & 3 deletions notebooks/chapter19/Optimizer and Backpropagation.ipynb
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Expand Up @@ -251,7 +251,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"images/backprop.png\" width=500/>"
"<img src=\"images/backprop.png\" width=\"500\"/>"
]
},
{
Expand All @@ -260,7 +260,7 @@
"source": [
"Applying optimizers and back-propagation algorithm together, we can update the weights of a neural network to minimize the loss function with alternatively doing forward and back-propagation process. Here is a figure form [here](https://medium.com/datathings/neural-networks-and-backpropagation-explained-in-a-simple-way-f540a3611f5e) describing how a neural network updates its weights:\n",
"\n",
"<img src=\"images/nn_steps.png\" width=700></img>"
"<img src=\"images/nn_steps.png\" width=\"700\"></img>"
]
},
{
Expand Down Expand Up @@ -303,7 +303,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
"version": "3.6.9"
}
},
"nbformat": 4,
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12 changes: 6 additions & 6 deletions notebooks/chapter19/RNN.ipynb
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Expand Up @@ -12,7 +12,7 @@
"\n",
"Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.\n",
"\n",
"<img src=\"images/rnn_unit.png\" width=500/>"
"<img src=\"images/rnn_unit.png\" width=\"500\"/>"
]
},
{
Expand All @@ -21,7 +21,7 @@
"source": [
"A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor. Consider what happens if we unroll the above loop:\n",
" \n",
"<img src=\"images/rnn_units.png\" width=500/>"
"<img src=\"images/rnn_units.png\" width=\"500\"/>"
]
},
{
Expand All @@ -30,7 +30,7 @@
"source": [
"As demonstrated in the book, recurrent neural networks may be connected in many different ways: sequences in the input, the output, or in the most general case both.\n",
"\n",
"<img src=\"images/rnn_connections.png\" width=700/>"
"<img src=\"images/rnn_connections.png\" width=\"700\"/>"
]
},
{
Expand Down Expand Up @@ -303,7 +303,7 @@
"\n",
"Autoencoders are an unsupervised learning technique in which we leverage neural networks for the task of representation learning. It works by compressing the input into a latent-space representation, to do transformations on the data. \n",
"\n",
"<img src=\"images/autoencoder.png\" width=800/>"
"<img src=\"images/autoencoder.png\" width=\"800\"/>"
]
},
{
Expand All @@ -314,7 +314,7 @@
"\n",
"Autoencoders have different architectures for different kinds of data. Here we only provide a simple example of a vanilla encoder, which means they're only one hidden layer in the network:\n",
"\n",
"<img src=\"images/vanilla.png\" width=500/>\n",
"<img src=\"images/vanilla.png\" width=\"500\"/>\n",
"\n",
"You can view the source code by:"
]
Expand Down Expand Up @@ -479,7 +479,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.6.9"
}
},
"nbformat": 4,
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12 changes: 6 additions & 6 deletions notebooks/chapter24/Image Edge Detection.ipynb
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Expand Up @@ -69,7 +69,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"images/gradients.png\" width=700/>"
"<img src=\"images/gradients.png\" width=\"700\"/>"
]
},
{
Expand Down Expand Up @@ -105,7 +105,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"images/stapler.png\" width=500/>\n",
"<img src=\"images/stapler.png\" width=\"500\"/>\n",
"\n",
"We will use `matplotlib` to read the image as a numpy ndarray:"
]
Expand Down Expand Up @@ -226,7 +226,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"images/derivative_of_gaussian.png\" width=400/>"
"<img src=\"images/derivative_of_gaussian.png\" width=\"400\"/>"
]
},
{
Expand Down Expand Up @@ -318,7 +318,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"images/laplacian.png\" width=200/>"
"<img src=\"images/laplacian.png\" width=\"200\"/>"
]
},
{
Expand All @@ -334,7 +334,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"images/laplacian_kernels.png\" width=300/>"
"<img src=\"images/laplacian_kernels.png\" width=\"300\"/>"
]
},
{
Expand Down Expand Up @@ -400,7 +400,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
"version": "3.6.9"
}
},
"nbformat": 4,
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6 changes: 3 additions & 3 deletions notebooks/chapter24/Objects in Images.ipynb
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Expand Up @@ -306,7 +306,7 @@
"source": [
"The bounding boxes are drawn on the original picture showed in the following:\n",
"\n",
"<img src=\"images/stapler_bbox.png\" width=500/>"
"<img src=\"images/stapler_bbox.png\" width=\"500\"/>"
]
},
{
Expand All @@ -324,7 +324,7 @@
"\n",
"[Ross Girshick et al.](https://arxiv.org/pdf/1311.2524.pdf) proposed a method where they use selective search to extract just 2000 regions from the image. Then the regions in bounding boxes are feed into a convolutional neural network to perform classification. The brief architecture can be shown as:\n",
"\n",
"<img src=\"images/RCNN.png\" width=500/>"
"<img src=\"images/RCNN.png\" width=\"500\"/>"
]
},
{
Expand Down Expand Up @@ -446,7 +446,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
"version": "3.6.9"
}
},
"nbformat": 4,
Expand Down