refactor: change model -> models for future implementations

Author: marcpinet

README.md

@@ -47,7 +47,7 @@ I used the [MNIST dataset](https://en.wikipedia.org/wiki/MNIST_database) to test
 ### Binary Classification
 
 ```python
-from neuralnetlib.model import Model
+from neuralnetlib.models import Model
 from neuralnetlib.layers import Input, Dense
 from neuralnetlib.activations import Sigmoid
 from neuralnetlib.losses import BinaryCrossentropy

examples/classification-regression/cancer_binary.ipynb

@@ -20,10 +20,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "is_executing": true,
     "ExecuteTime": {
      "start_time": "2024-11-10T12:30:01.871174100Z"
-    }
+    },
+    "is_executing": true
    },
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
     "from neuralnetlib.layers import Input, Activation, Dense, BatchNormalization\n",
     "from neuralnetlib.callbacks import EarlyStopping\n",
     "from neuralnetlib.losses import BinaryCrossentropy\n",
-    "from neuralnetlib.model import Model\n",
+    "from neuralnetlib.modelss import Model\n",
     "from neuralnetlib.optimizers import Adam\n",
     "from neuralnetlib.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
     "from neuralnetlib.utils import train_test_split"

examples/classification-regression/diabete_regression.ipynb

@@ -34,7 +34,7 @@
     "from neuralnetlib.activations import Linear, LeakyReLU\n",
     "from neuralnetlib.layers import Input, Dense, Activation\n",
     "from neuralnetlib.losses import MeanSquaredError, MeanAbsoluteError\n",
-    "from neuralnetlib.model import Model\n",
+    "from neuralnetlib.models import Model\n",
     "from neuralnetlib.optimizers import Adam\n",
     "from neuralnetlib.utils import train_test_split"
    ]

examples/classification-regression/mnist_loading_saved_model.ipynb

@@ -29,7 +29,7 @@
    "source": [
     "from tensorflow.keras.datasets import mnist  # Dataset for testing\n",
     "\n",
-    "from neuralnetlib.model import Model\n",
+    "from neuralnetlib.models import Model\n",
     "from neuralnetlib.preprocessing import one_hot_encode\n",
     "from neuralnetlib.utils import train_test_split\n",
     "from neuralnetlib.metrics import accuracy_score, confusion_matrix"

examples/classification-regression/mnist_multiclass.ipynb

@@ -34,7 +34,7 @@
     "from neuralnetlib.activations import Sigmoid, Softmax\n",
     "from neuralnetlib.layers import Input, Dense, Activation, Dropout\n",
     "from neuralnetlib.losses import CategoricalCrossentropy\n",
-    "from neuralnetlib.model import Model\n",
+    "from neuralnetlib.models import Model\n",
     "from neuralnetlib.optimizers import SGD\n",
     "from neuralnetlib.preprocessing import one_hot_encode\n",
     "from neuralnetlib.metrics import accuracy_score, f1_score, recall_score"

examples/classification-regression/sentiment_analysis.ipynb

@@ -30,7 +30,7 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
-    "from neuralnetlib.model import Model\n",
+    "from neuralnetlib.models import Model\n",
     "from neuralnetlib.layers import Input, Dense, Embedding, LSTM, Bidirectional, Attention, GlobalAveragePooling1D\n",
     "from neuralnetlib.preprocessing import Tokenizer, pad_sequences\n",
     "from neuralnetlib.metrics import accuracy_score\n",

examples/cnn-classification/cnn_classification_mnist.ipynb

@@ -33,7 +33,7 @@
     "\n",
     "from neuralnetlib.activations import ReLU\n",
     "from neuralnetlib.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Activation\n",
-    "from neuralnetlib.model import Model\n",
+    "from neuralnetlib.models import Model\n",
     "from neuralnetlib.preprocessing import one_hot_encode\n",
     "from neuralnetlib.metrics import accuracy_score, f1_score, recall_score\n",
     "from neuralnetlib.utils import train_test_split"

examples/real-life-applications/dinosaurnames/dinosaur_names_generator.ipynb

@@ -16,7 +16,7 @@
     "import numpy as np\n",
     "import random\n",
     "from neuralnetlib.layers import Input, Embedding, LSTM, Dense\n",
-    "from neuralnetlib.model import Model\n",
+    "from neuralnetlib.models import Model\n",
     "from neuralnetlib.preprocessing import one_hot_encode\n",
     "from neuralnetlib.callbacks import EarlyStopping"
    ]

examples/real-life-applications/handwrittendigits/tkinter_gui_handwritten.py

@@ -3,7 +3,7 @@
 import numpy as np
 from PIL import Image, ImageDraw
 
-from neuralnetlib.model import Model
+from neuralnetlib.modelss import Model
 
 
 def main():

examples/real-life-applications/tictactoe/tic_tac_toe_alternative_dataset_shape.py

@@ -8,7 +8,7 @@
 from neuralnetlib.layers import Input, Dense, Activation
 from neuralnetlib.losses import BinaryCrossentropy
 from neuralnetlib.metrics import accuracy_score
-from neuralnetlib.model import Model
+from neuralnetlib.modelss import Model
 from neuralnetlib.optimizers import Adam
 from neuralnetlib.callbacks import EarlyStopping