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@koaning koaning/
Created Nov 16, 2019

What would you like to do?
keras grid job
import uuid
import json
import random
import keras
import numpy as np
import tensorflow as tf
import click
def build_model(blob):
inputs = keras.Input(shape=(blob['num_columns'],), name='img')
x = keras.layers.Dense(blob['num_columns'], activation='sigmoid')(inputs)
for i in range(blob['num_layers']-1):
x = keras.layers.Dense(blob['num_columns'], activation='sigmoid')(x)
outputs = keras.layers.Dense(blob['num_columns'], activation='sigmoid')(x)
model = keras.Model(inputs=inputs, outputs=outputs, name='mnist_model')
model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(learning_rate=0.1))
return model
def train_model(mod, blob):
train = np.random.randint(0, 2, (blob['rows'], blob['num_columns']))
mod.compile(loss=blob['loss_func'], optimizer=keras.optimizers.Adam(learning_rate=0.1))
return, train,
callbacks=[keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5,
patience=15, verbose=2,
mode='auto', min_delta=0.01)])
def build_train(blob):
model = build_model(blob)
hist = train_model(model, blob)
blob['lr'] = [float(i) for i in blob['lr']]
return blob
if __name__ == "__main__":
res = build_train({'num_columns': random.choice([10, 11, 12]), 'num_layers': random.choice([1, 3, 2, 3, 4, 4, 5, 5]),
'loss_func': 'binary_crossentropy', 'tf_seed': random.randint(1, 4200),
'epochs': 300, 'rows': random.choice([2000, 3000, 4000])})
with open(f"/Users/vincent/Development/grid/logs/{str(uuid.uuid4())[:14]}.jsonl", "w") as f:
json.dump(res, f)
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