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import tensorrt |
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import tensorflow as tf |
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from tensorflow import keras |
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from keras import layers |
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from keras import activations |
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from keras.datasets import mnist |
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from keras.utils import to_categorical |
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from keras.models import Sequential |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from sklearn.metrics import confusion_matrix |
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import pandas as pd |
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import gradio as gr |
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from gradio import Interface |
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import tensorflow as tf |
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from tensorflow import keras |
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from tensorflow.keras import datasets, layers, models |
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import numpy as np |
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import pandas as pd |
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import matplotlib.image as mpimg |
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X_train = pd.read_csv("csvTrainImages 60k x 784.csv") |
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y_train = pd.read_csv("csvTrainLabel 60k x 1.csv") |
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X_test = pd.read_csv("csvTestImages 10k x 784.csv") |
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y_test = pd.read_csv("csvTestLabel 10k x 1.csv") |
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print('X_train shape', X_train.shape) |
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print('y_train shape', y_train.shape) |
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print('X_test shape', X_test.shape) |
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print('y_test shape', y_test.shape) |
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X_train /= 255 |
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X_test /= 255 |
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X_train = X_train.values.reshape(-1,28,28,1) |
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X_test = X_test.values.reshape(-1,28,28,1) |
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X_train = X_train.astype('float32') |
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X_test = X_test.astype('float32') |
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print("Training matrix shape", X_train.shape) |
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print("Testing matrix shape", X_test.shape) |
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nb_classes = 10 |
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Y_train = to_categorical(y_train, nb_classes) |
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Y_test = to_categorical(y_test, nb_classes) |
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print(nb_classes) |
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print(Y_train.shape) |
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print(Y_test.shape) |
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import sklearn |
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from sklearn.model_selection import train_test_split |
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X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=4) |
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print(X_train.shape) |
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print(Y_train.shape) |
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print(X_val.shape) |
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print(Y_val.shape) |
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model = Sequential() |
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model.add(layers.Conv2D(filters = 80, kernel_size = (5,5),padding = 'Same', activation ='relu', input_shape = (28,28,1))) |
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model.add(layers.MaxPool2D(pool_size=(2,2))) |
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model.add(layers.Dropout(0.25)) |
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model.add(layers.Conv2D(filters = 64, kernel_size = (5,5),padding = 'Same', activation ='relu')) |
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model.add(layers.MaxPool2D(pool_size=(2,2))) |
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model.add(layers.Dropout(0.25)) |
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model.add(layers.Flatten()) |
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model.add(layers.Dense(128, activation = "relu")) |
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model.add(layers.Dropout(0.25)) |
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model.add(layers.Dense(10, activation = "softmax")) |
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model.summary() |
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from tensorflow.keras.optimizers import SGD |
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optimizer = SGD(learning_rate=0.001, momentum=0.30) |
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model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"]) |
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history = model.fit( X_train,Y_train, batch_size=64, epochs = 10, validation_data = (X_val, Y_val), verbose = 1) |
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def predict_image(img): |
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img_3d = img.reshape(-1, 28,28) |
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img_scaled = img_3d/255 |
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prediction = model.predict(img_scaled) |
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pred = np.argmax(prediction) |
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return pred.item() |
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sp = gr.Sketchpad(brush_radius=1.0, shape=(28,28), ) |
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iface = gr.Interface(predict_image, inputs=sp, outputs='label', title='Arabic Numbers Recognition', description='Draw a number') |
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iface.launch() |
