Spaces:
Runtime error
Runtime error
import gradio as gr | |
import tensorflow as tf | |
from matplotlib import pyplot as plt | |
import numpy as np | |
num_objects = tf.keras.datasets.mnist | |
(training_images, training_labels), (test_images, test_labels) = num_objects.load_data() | |
for i in range(9): | |
#define subplot | |
plt.subplot(330 + 1 + i) | |
#plot of raw pixel data | |
plt.imshow(training_images[i]) | |
training_images = training_images / 255.0 | |
test_images = test_images / 255.0 | |
from tensorflow.keras.layers import Flatten, Dense | |
model = tf.keras.models.Sequential([Flatten(input_shape=(28,28)), | |
Dense(256, activation='relu'), | |
Dense(256, activation='relu'), | |
Dense(128, activation='relu'), | |
Dense(10, activation=tf.nn.softmax)]) | |
model.compile(optimizer = 'adam', | |
loss = 'sparse_categorical_crossentropy', | |
metrics=['accuracy']) | |
model.fit(training_images, training_labels, epochs=10) #how many times u go through the dataset | |
test=test_images[0].reshape(-1,28,28) | |
pred=model.predict(test) | |
print(pred) | |
def predict_image(img): | |
img_3d=img.reshape(-1,28,28) | |
im_resize=img_3d/255.0 | |
prediction=model.predict(im_resize) | |
pred=np.argmax(prediction) | |
return pred | |
iface = gr.Interface(predict_image, inputs="sketchpad", outputs="label") | |
iface.launch(debug='True') |