gradio/Test Tensorflow.ipynb

In [1]:

%load_ext autoreload
%autoreload 2

import tensorflow as tf
import gradio

In [2]:

n_classes = 10
(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train, x_test = x_train.reshape(-1, 784) / 255.0, x_test.reshape(-1, 784) / 255.0
y_train = tf.keras.utils.to_categorical(y_train, n_classes).astype(float)
y_test = tf.keras.utils.to_categorical(y_test, n_classes).astype(float)

learning_rate = 0.5
epochs = 5
batch_size = 100

In [3]:

x = tf.placeholder(tf.float32, [None, 784], name="x")
y = tf.placeholder(tf.float32, [None, 10], name="y")

W1 = tf.Variable(tf.random_normal([784, 300], stddev=0.03), name='W1')
b1 = tf.Variable(tf.random_normal([300]), name='b1')
W2 = tf.Variable(tf.random_normal([300, 10], stddev=0.03), name='W2')
hidden_out = tf.add(tf.matmul(x, W1), b1)
hidden_out = tf.nn.relu(hidden_out)
y_ = tf.matmul(hidden_out, W2)

WARNING:tensorflow:From C:\Users\ALI\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.

In [4]:

probs = tf.nn.softmax(y_)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=y_, labels=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cross_entropy)

In [5]:

init_op = tf.global_variables_initializer()
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

In [6]:

sess = tf.Session()
sess.run(init_op)
total_batch = int(len(y_train) / batch_size)
for epoch in range(epochs):
    avg_cost = 0
    for start, end in zip(range(0, len(y_train), batch_size), range(batch_size, len(y_train)+1, batch_size)):        
        batch_x = x_train[start: end]
        batch_y = y_train[start: end]
        _, c = sess.run([optimizer, cross_entropy], feed_dict={x: batch_x, y: batch_y})
        avg_cost += c / total_batch

In [7]:

def predict(inp):
    return sess.run(probs, feed_dict={x:inp})

In [8]:

inp = gradio.inputs.Sketchpad(flatten=True)
io = gradio.Interface(inputs=inp, outputs="label", model_type="pyfunc", model=predict)

In [9]:

io.launch(share=True)

No validation samples for this interface... skipping validation.
NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu
Model is running locally at: http://localhost:7860/interface.html
Model available publicly for 8 hours at: https://share.gradio.app/25d5d472

Out[9]:

(<gradio.networking.serve_files_in_background.<locals>.HTTPServer at 0x229f97553c8>,
 'http://localhost:7860/',
 'http://25d5d472.ngrok.io')

In [ ]: