gistfile1.txt

import tensorflow as tf

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(32, activation='relu', input_dim=100))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer=tf.keras.optimizers.Adadelta(rho=0.9),
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Generate dummy data
import numpy as np
data = np.random.random((1000, 100))
labels = np.random.randint(2, size=(1000, 1))

# Train the model, iterating on the data in batches of 32 samples
model.fit(data, labels, epochs=10, batch_size=32)S)