<commit_before># Dog-Cat Classifier
### By <NAME>

Dog and cat image classifier with deep learning.<br/>

#### Example:
| <img src="test_dog.jpg?raw=true" width="200">|<img src="test_cat.jpg?raw=true" width="200">|
|:-:|:-:|
|Dog: 0.92035621<br/>Cat: 0.04618423|Cat: 0.90135497<br/>Dog: 0.09642436|

###### Layer outputs of test photographs:

| <img src="Data/Layer_Outputs/Dog/Layer_1_Outputs/4.jpg?raw=true" width="32"> <img src="Data/Layer_Outputs/Cat/Layer_1_Outputs/4.jpg?raw=true" width="32">| <img src="Data/Layer_Outputs/Dog/Layer_2_Outputs/16.jpg?raw=true" width="32"> <img src="Data/Layer_Outputs/Cat/Layer_2_Outputs/16.jpg?raw=true" width="32">| <img src="Data/Layer_Outputs/Dog/Layer_3_Outputs/10.jpg?raw=true" width="32"> <img src="Data/Layer_Outputs/Cat/Layer_3_Outputs/10.jpg?raw=true" width="32">|
|::|
|Layer: 1<br/>Kernel: 4|Layer: 2<br/>Kernel: 16|Layer: 3<br/>Kernel: 10|

Note: This layers made of Convolution, Activation and Max Pooling layers.

### Using Predict Command:
`python3 predict.py <ImageFileName>`

### Model Training:
`python3 train.py`

### Using TensorBoard:
`tensorboard --logdir=Data/Checkpoints/./logs`

### Model Architecture:
- Input Data
Shape: 64x64x3

Layer 1:
- Convolutional Layer
32 filter<commit_msg>README.md file updated<commit_after># Dog-Cat Classifier
### By <NAME>

Dog and cat image classifier with deep learning.<br/>

#### Example:
| <img src="test_dog.jpg?raw=true" width="200">|<img src="test_cat.jpg?raw=true" width="200">|
|:-:|:-:|
|Dog: 0.92035621<br/>Cat: 0.04618423|Cat: 0.90135497<br/>Dog: 0.09642436|

###### Layer outputs of test photographs:

| <img src="Data/Layer_Outputs/Dog/Layer_1_Outputs/4.jpg?raw=true" width="32"> <img src="Data/Layer_Outputs/Cat/Layer_1_Outputs/4.jpg?raw=true" width="32">| <img src="Data/Layer_Outputs/Dog/Layer_2_Outputs/16.jpg?raw=true" width="32"> <img src="Data/Layer_Outputs/Cat/Layer_2_Outputs/16.jpg?raw=true" width="32">| <img src="Data/Layer_Outputs/Dog/Layer_3_Outputs/10.jpg?raw=true" width="32"> <img src="Data/Layer_Outputs/Cat/Layer_3_Outputs/10.jpg?raw=true" width="32">|
|:--:|:--:|:--:|
|Layer: 1<br/>Kernel: 4|Layer: 2<br/>Kernel: 16|Layer: 3<br/>Kernel: 10|

Note: This layers made of Convolution, Activation and Max Pooling layers.

### Using Predict Command:
`python3 predict.py <ImageFileName>`

### Model Training:
`python3 train.py`

### Using TensorBoard:
`tensorboard --logdir=Data/Checkpoints/./logs`

### Model Architecture:
- Input Data
Shape: 64x64x3

Layer 1:
- Convolutional Layer
32 filter
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{'dir': 'git-commits-cleaned', 'id': '2417775', 'n_tokens_mistral': 1092, 'n_tokens_neox': 956, 'n_words': 183}