DataBloom

Hi, everyone, hope you are doing well. I am new to Machine Learning and Tensorflow. I was wondering if anyone wants to team up or include me in your team. I would be very grateful. I want to work on a real-life project and this seems to be the best. Thank You.

Is anyone here able to help me out make a tensorflow lite object detection model I can run on my pi? I have all of the training data collected and labeled just need help making the model.

I have tried a few things including the Tensorflow Lite Model Maker as well as doing it from scratch locally. Just need help making my model.

Afternoon!

I would like to create an app around community based image feedback. Is it possible to create a model around what the community rates your existing images & use it to tentatively give a new image a score before anyone votes on it? Can I also incorporate other factors in the image, such as distance between objects or color of items to further refine the model later on?

I’m trying to create a neural network where given the pure initial state of a quantum circuit (2D-vector), it spits out 2 numbers that would be essentially fed into a quantum computer to get results out. Currently, I have the step where I send a query to the quantum computer as a layer in the model. The quantum computer then will (in this case) spit out two numbers which I’d compare to the theoretical values.

As I was trying to implement this using the functional API, my custom layer was yelling at my because it did not like me using tf.unstack when it tried to build it by passing through a variable tensor. While this would probably be somewhat simple to fix, I ‘m concerned about how Qiskit would react to the values from the variable tensor or if it would work at all. Are there any workarounds or already implemented functions to achieve this?

Code:

Data Generation:

# TODO extend to complex amplitudes def generate_Hadamard_data(num_data): data = [] for _ in range(num_data): data_elem = preprocessing.normalize(np.random.rand(1, 2)).tolist()[0] data.append(data_elem) inv_sq2 = 1/np.sqrt(2) hadamard = np.array([[inv_sq2, inv_sq2], [inv_sq2, -1*inv_sq2]]) output = [np.matmul(hadamard, state).tolist() for state in data] data_ten = tf.constant(np.array(data).T, dtype=tf.float32) output_ten = tf.constant(np.array(output).T, dtype=tf.float32) print(output_ten) return tf.data.Dataset.from_tensor_slices(data_ten), tf.data.Dataset.from_tensor_slices(output_ten) 

Custom Layer:

class HadamardCircuitLayer(tf.keras.layers.Layer): ''' Takes in the initial data (state) and output (pulse params) from the neural network and runs it on the backend ibmq_armonk ''' def __init__(self, initial_data, shots=1024): super(HadamardCircuitLayer, self).__init__() # Unpack data data_iterator = initial_data.as_numpy_iterator() self.zero_coeffs = data_iterator.next() self.one_coeffs = data_iterator.next() self.shots = shots def build(self, shape): pass def call(self, output): custom_gate = Gate('custom_gate', 1, []) provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main') backend = provider.get_backend('ibmq_armonk') post_q_list = [] for a, b, pulse_params in zip(self.zero_coeffs, self.one_coeffs, tf.unstack(output)): norm = np.sqrt(a**2 + b**2) qc = QuantumCircuit(1, 1) qc.initialize([a/norm, b/norm], 0) qc.append(custom_gate, [0]) qc.measure(0, 0) pul = pulse_params.numpy() with pulse.build(backend, name='custom') as my_schedule: pulse.play(Gaussian(duration=64, amp=pul[0], sigma=np.e**pul[1]), pulse.drive_channel(0)) qc.add_calibration(custom_gate, [0], my_schedule) qc = transpile(qc, backend) job = execute(qc, backend=backend, shots=self.shots) counts = job.result().get_counts() post_q_list.append(np.array([counts["0"]/self.shots, counts["1"]/self.shots])) output_qten = tf.constant(post_q_list, dtype=tf.float32) print(output_qten) return output_qten 

Implementation:

# I haven't gotten far since running into the unstack error x_train, y_train = generate_Hadamard_data(1) dataset = tf.data.Dataset.zip((x_train, y_train)) inputs = tf.keras.Input(shape=(2,)) x = tf.keras.layers.Dense(128, activation="linear")(inputs) x = tf.keras.layers.Dropout(0.2)(x) x = tf.keras.layers.Dense(128, activation="linear")(x) x = tf.keras.layers.Dense(2, activation="linear")(x) outputs = HadamardCircuitLayer(x_train)(x) model = tf.keras.Model(inputs=inputs, outputs=outputs, name="microwave_pulse_model") 

I am a mobile app developer. I have been working in IT for past 5 years. I took a Udemy course on TensforFlow from Zero to mastery. I thought as I have decent knowledge of software development I might pick up TensorFlow pretty quickly without really knowing the basics of machine learning but I was so wrong as I am having tough time understanding Tensorflow from the course. Everyone keep saying learn linear algebra, pandas, keras , scikit learn etc and a bunch of stuff. This is too much for me. For now I just want to learn how to create an ML model with a given data(Data can be anything image, text etc) and use that model in my web and mobile apps. I know there is something call TensorFlow lite which I can use in my apps directly but what is the bare minimum requirement I need to know before I start learning TensorFlow so I can easily pick it up later.

Also the the Udemy course which I took seems to be pretty good and lot of people seems to be liking it so I don’t think it really is the instructor’s fault as I don’t have my basics clear.

If anyone has any udemy courses that they can point me to would be great. I am looking more on practical approach and not jus theory boring stuff