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import pandas as pd
import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Concatenate
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Convolution2D
from tensorflow.keras.layers import Flatten
# 30 10(11) 5
# 27 17 4
def make_model(size, magic=6):
# Our model for the stacks, a small CNN
stack_shape = (size, size, 8)
stack_input = Input(shape=stack_shape)
stack_model = Convolution2D(magic, kernel_size=(3, 3), strides=(1, 1),
padding='valid', activation="relu",
use_bias=True)(stack_input)
stack_model = Flatten()(stack_model)
# stack_model = Dense(magic, activation="relu", use_bias=True)(stack_model)
stack_model = Model(inputs=stack_input, outputs=stack_model)
# The overall model
flats_input = Input(shape=(2,))
combn_input = Concatenate()([stack_model.output, flats_input])
model = Dense(magic+2, activation="relu", use_bias=True)(combn_input)
model = Dense(magic, activation="relu", use_bias=True)(model)
model = Dense(2, activation="sigmoid", use_bias=True)(model)
model = Model(inputs=[stack_model.input, flats_input], outputs=model)
model.compile(optimizer='adam',
loss='binary_crossentropy',
# loss='categorical_crossentropy',
# loss='mean_squared_error',
metrics=['accuracy'])
model.summary()
return model
def train(size, model, reps):
shape = (-1, size, size, 8)
tr_fn = "training-"+str(size)+".csv"
training_csv = pd.read_csv(tr_fn) # .head(80000)
training_data = training_csv
training_stack_input = np.array(training_data.iloc[:, 2:-2]).reshape(shape)
training_flats_input = np.array(training_data.iloc[:, 0:2])
training_input = [training_stack_input, training_flats_input]
training_outcome = training_data.iloc[:, -2:]
val_fn = "validation-"+str(size)+".csv"
val_data = pd.read_csv(val_fn).tail(20000)
val_stack_input = np.array(val_data.iloc[:, 2:-2]).reshape(shape)
val_flats_input = np.array(val_data.iloc[:, 0:2])
val_input = [val_stack_input, val_flats_input]
val_outcome = val_data.iloc[:, -2:]
results = []
for i in range(0, reps):
print("Iteration {0}/{1}".format(i+1, reps))
model.fit(training_input, training_outcome, epochs=5,
validation_data=(val_input, val_outcome),
verbose=True)
v_loss, v_accuracy = model.evaluate(val_input, val_outcome, verbose=False)
t_loss, t_accuracy = model.evaluate(training_input, training_outcome,
verbose=False)
results += [((v_loss, t_loss), (v_accuracy, t_accuracy))]
print("\n\n")
for i in range(0, reps):
print("Iteration {0}/{1}: {2}".format(i+1, reps, results[i]))
return model
# model = train(5, make_model(5), 50)
# print(model.trainable_variables)
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