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import numpy as np
import gradio as gr
from PIL import Image
def ReLU(Z):
return np.maximum(Z, 0)
def softmax(Z):
A = np.exp(Z) / np.sum(np.exp(Z), axis=0)
return A
def init_params():
W1 = np.random.rand(10, 784) - 0.5
b1 = np.random.rand(10, 1) - 0.5
W2 = np.random.rand(10, 10) - 0.5
b2 = np.random.rand(10, 1) - 0.5
return W1, b1, W2, b2
def forward_prop(W1, b1, W2, b2, X):
Z1 = W1.dot(X) + b1
A1 = ReLU(Z1)
Z2 = W2.dot(A1) + b2
A2 = softmax(Z2)
return Z1, A1, Z2, A2
def get_predictions(A2):
return np.argmax(A2, axis=0)
def make_predictions(X, W1, b1, W2, b2):
_, _, _, A2 = forward_prop(W1, b1, W2, b2, X)
predictions = get_predictions(A2)
return predictions
def predict_digit(img):
# Load the trained parameters
params = np.load("trained_params.npz", allow_pickle=True)
W1, b1, W2, b2 = params["W1"], params["b1"], params["W2"], params["b2"]
# Convert the sketchpad drawing to grayscale and resize it to (28, 28)
img_pil = Image.fromarray(np.uint8(img * 255)).convert("L").resize((28, 28))
# Convert the image to a NumPy array and normalize it
X = np.array(img_pil).reshape((784, 1)) / 255.
# Get the prediction
prediction = make_predictions(X, W1, b1, W2, b2)
return int(prediction)
iface = gr.Interface(
fn=predict_digit,
inputs="sketchpad",
outputs=gr.outputs.Label(num_top_classes=3),
live=True,
capture_session=True,
title="Handwritten Digit Recognizer",
description="Draw a digit using your mouse, and the model will try to recognize it.",
)
if __name__ == "__main__":
iface.launch()
'''import numpy as np
import gradio as gr
from PIL import Image
def ReLU(Z):
return np.maximum(Z, 0)
def softmax(Z):
A = np.exp(Z) / sum(np.exp(Z))
return A
def init_params():
W1 = np.random.rand(10, 784) - 0.5
b1 = np.random.rand(10, 1) - 0.5
W2 = np.random.rand(10, 10) - 0.5
b2 = np.random.rand(10, 1) - 0.5
return W1, b1, W2, b2
def forward_prop(W1, b1, W2, b2, X):
Z1 = W1.dot(X) + b1
A1 = ReLU(Z1)
Z2 = W2.dot(A1) + b2
A2 = softmax(Z2)
return Z1, A1, Z2, A2
def get_predictions(A2):
return np.argmax(A2, 0)
def make_predictions(X, W1, b1, W2, b2):
_, _, _, A2 = forward_prop(W1, b1, W2, b2, X)
predictions = get_predictions(A2)
return predictions
def predict_digit(img):
# Load the trained parameters
params = np.load("trained_params.npz", allow_pickle=True)
W1, b1, W2, b2 = params["W1"], params["b1"], params["W2"], params["b2"]
# Convert the sketchpad drawing to grayscale and resize it to (28, 28)
img_pil = Image.fromarray(np.uint8(img * 255)).convert("L").resize((28, 28))
# Convert the image to a NumPy array and normalize it
X = np.array(img_pil).reshape((784, 1)) / 255.
# Get the prediction
prediction = make_predictions(X, W1, b1, W2, b2)
return int(prediction)
iface = gr.Interface(
fn=predict_digit,
inputs="sketchpad",
outputs=gr.outputs.Label(num_top_classes=3),
live=True,
capture_session=True,
title="Handwritten Digit Recognizer",
description="Draw a digit using your mouse, and the model will try to recognize it.",
)
if __name__ == "__main__":
iface.launch()
''' |