def train_model(self, *, caller_file: str):
"""
Perform model training on data from input_file.
Pass __file__ variable of the caller script as caller_file
parameter. It will be used as output file prefix.
"""
# Make numpy printouts easier to read
np.set_printoptions(precision=3, suppress=True)
# Set random seed for both Python and TF
# to make the results reproducible
seed = 0
np.random.RandomState(seed)
tf.random.set_seed(seed)
# Input file has the same name as the caller script
# and csv extension, unless specified otherwise.
if self.input_file is None:
input_file = f"{FileUtil.get_caller_name(caller_file=caller_file)}.csv"
else:
input_file = self.input_file
# Read the dataset
self.__input_dataset = pd.read_csv(input_file)
# Skip the specified number of samples
if self.skip_samples is not None:
self.__input_dataset = self.__input_dataset.tail(self.skip_samples)
# Then take the specified number of samples
if self.take_samples is not None:
self.__input_dataset = self.__input_dataset.head(self.take_samples)
# Convert LOCATION column to one-hot encoding to avoid
# model bias due to the currency order in sample.
self.__train_dataset = self.__input_dataset.copy()
# Split features from labels
# Remove target series from train dataset and save it to a separate variable
target_series = self.__train_dataset.pop(self.__lag_short_rate_feature)
# Create a normalizer layer and adapt it to data
short_rate = np.array(self.__train_dataset[self.__short_rate_feature])
normalizer = preprocessing.Normalization(input_shape=[
1,
])
normalizer.adapt(short_rate)
# Create DNN model (not yet deep in this example)
self.__model = keras.Sequential([
normalizer,
layers.Dense(64, activation='sigmoid'),
layers.Dense(1)
])
# Compile the model
self.__model.compile(loss='mean_squared_error',
optimizer=tf.keras.optimizers.Adam(
self.learning_rate))
# Print model summary
print(self.__model.summary())
# Perform training and save training history in a variable
# Fit is performed by using validation_split fraction of the data
# to train and the remaining data to minimize.
training_history = self.__model.fit(
self.__train_dataset[self.__short_rate_feature],
target_series,
validation_split=0.5,
verbose=0,
epochs=100)
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
return preprocessing.Normalization(axis=self.axis)(input_node)
# The below code is the equivalent of... # horsepower_model.compile( # optimizer=DummyOptimizer(), # loss='mean_absolute_error') # history = horsepower_model.fit( # train_features['Horsepower'][:10], train_labels[:10], # epochs=100, # Number of epochs really doesn't matter # # suppress logging # verbose=0) # Which yields a mean absolute error of 26.834576 # See horsepower.py for construction of this code horsepower_normalizer = preprocessing.Normalization() horsepower_normalizer.adapt(horsepower) kernel = initializer(shape=(1, 1)) input_value = horsepower[:10] input_value = tf.reshape(input_value, [10, 1]) input_value = tf.cast(input_value, tf.float32) normalized_value = horsepower_normalizer(horsepower[:10]) matrix_product = tf.tensordot(normalized_value, kernel, 1) # tf.print(matrix_product) horsepower_matrix = np.array(matrix_product) # print(array) # def calculate_mae(dataset1, dataset2, weight = 1): # mae_results = []
# if i == 0:
# a = tf.zeros(result.shape)
# train_data = tf.raw_ops.Add(x=a, y=result)
# else:
# train_data = tf.concat([train_data, result], axis=0)
# print("\r{}th file is done...".format(i+1), end='')
result = np.expand_dims(train_data, -1)
# result = tf.raw_ops.ExpandDims(train_data, -1)
print(result.shape)
x = preprocessing.Resizing(32, 32)(result)
print(x.shape)
x = preprocessing.Normalization()(x)
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.MaxPooling2D()(x)
x = layers.Dropout(0.25)(x)
x = layers.Flatten()(x)
x = layers.Dense(128, activation='relu')(x)
x = layers.Dropout(0.5)(x)
answer = layers.Dense(7, activation='softmax')(x)
model = keras.Model(inputs=input_sigss, outputs=answer)
model.summary()
keras.utils.plot_model(model,
"proto_model_with_shape_info.png",
import numpy as np
# Make numpy values easier to read.
np.set_printoptions(precision=3, suppress=True)
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
titanic = pd.read_csv(
"https://storage.googleapis.com/tf-datasets/titanic/train.csv")
titanic.head()
titanic_features = titanic.copy()
titanic_labels = titanic_features.pop('survived')
normalize = preprocessing.Normalization()
# Create a symbolic input
input = tf.keras.Input(shape=(), dtype=tf.float32)
# Do a calculation using is
result = 2 * input + 1
# the result doesn't have a value
result
calc = tf.keras.Model(inputs=input, outputs=result)
print(calc(1).numpy())
print(calc(2).numpy())
def get_normalization_layer(name, dataset):
normalizer = preprocessing.Normalization()
feature_ds = dataset.map(lambda x, y: x[name])
normalizer.adapt(feature_ds)
return normalizer
test_dataset = dataset.drop(train_dataset.index)
# Diagnostics Plot
#sns.pairplot(train_dataset[['estimated_power', 'actual_position', 'position_error', 'output_force']], diag_kind='kde')
print("\n\nTransposing Dataset - Overall Statistics:")
print(train_dataset.describe().transpose())
train_features = train_dataset.copy()
test_features = test_dataset.copy()
train_labels = train_features.pop('weight')
test_labels = test_features.pop('weight')
# Normalize the data - Sample of how it looks
normalizer = preprocessing.Normalization()
normalizer.adapt(np.array(train_features))
# Print demo for normalized values
#first = np.array(train_features[:1])
#with np.printoptions(precision=2, suppress=True):
# print('\nFirst example:', first)
# print()
# print('\nNormalized:', normalizer(first).numpy())
# Normalize Output Force
outputforce = np.array(train_features['output_force'])
outputforce_normalizer = preprocessing.Normalization(input_shape=[
1,
])
outputforce_normalizer.adapt(outputforce)
test_ds = preprocess_dataset(test_files)
batch_size = 64
train_ds = train_ds.batch(batch_size)
val_ds = val_ds.batch(batch_size)
train_ds = train_ds.cache().prefetch(AUTOTUNE)
val_ds = val_ds.cache().prefetch(AUTOTUNE)
for spectrogram, _ in spectrogram_ds.take(1):
input_shape = spectrogram.shape
print('Input shape:', input_shape)
num_labels = len(commands)
norm_layer = preprocessing.Normalization()
norm_layer.adapt(spectrogram_ds.map(lambda x, _: x))
model = models.Sequential([
layers.Input(shape=input_shape),
#preprocessing.Resizing(32, 32),
norm_layer,
layers.Conv2D(32, 3, activation='relu'),
layers.Conv2D(64, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Dropout(0.25),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dropout(0.5),
layers.Dense(num_labels),
])
def baseline():
showPlot = False
np.set_printoptions(precision=3, suppress=True)
mobility_dataframe = pd.read_csv('google_baseline_test.csv',
infer_datetime_format=True,
parse_dates=True)
cdc_dataframe = pd.read_csv('cdc_baseline_test_movingAvg.csv',
infer_datetime_format=True,
parse_dates=True)
full_dataframe = pd.concat([mobility_dataframe, cdc_dataframe], axis=1)
#sns.pairplot(full_dataframe[['newAndPnew', 'retail_and_recreation_percent_change_from_baseline', 'workplaces_percent_change_from_baseline', 'residential_percent_change_from_baseline']], diag_kind='kde')
#plt.show()
bestLinearCorr = 0
bestLogCorr = 0
bestLinearOffset = -1
bestLogOffset = -1
bestLinearData = 0
bestLogData = 0
correlationScores = []
correlationLogScores = []
for offset in range(100):
#Shift CDC data by offset value
cdc_dataframe_truc = cdc_dataframe.shift(periods=offset, fill_value=0)
#Build new full data array
mobility_dataframe_truc = mobility_dataframe.drop(columns=['date'])
full_dataframe = pd.concat(
[cdc_dataframe_truc, mobility_dataframe_truc], axis=1)
full_dataframe['originalCases'] = cdc_dataframe[
'newAndPnew'] #preserve original case values as additional feature
full_dataframe_noDate = full_dataframe.drop(
columns=['submission_date'])
full_dataframe_noDate = full_dataframe_noDate.loc[(
full_dataframe_noDate['newAndPnew'] !=
0)] #remove rows with zero cases
#Compute linear and logatrithmic correlations
linearCorr = full_dataframe_noDate.corr()
linearCorr = linearCorr.to_numpy()[
0, 1:] #Take only correlations between 'cases' and mobility data
logData = np.log(full_dataframe_noDate + 1 -
np.min(full_dataframe_noDate.to_numpy()))
logCorr = logData.corr()
logCorr = logCorr.to_numpy()[
0, 1:] #Take only correlations between 'cases' and mobility data
print("Offset:", offset, "Correlation: ", linearCorr)
print(" Log Correlation:", logCorr)
#Save best values
if np.linalg.norm(linearCorr) > np.linalg.norm(bestLinearCorr):
bestLinearCorr = linearCorr
bestLinearOffset = offset
bestLinearData = full_dataframe_noDate
if np.linalg.norm(logCorr) > np.linalg.norm(bestLogCorr):
bestLogCorr = logCorr
bestLogOffset = offset
bestLogData = logData
correlationScores.append(np.linalg.norm(linearCorr))
correlationLogScores.append(np.linalg.norm(logCorr))
if showPlot:
plt.plot(correlationScores)
plt.xlabel("Cases offset (days)")
plt.ylabel("Norm of correlation vector")
plt.title("Linear correlation vs. data offset")
plt.show()
plt.plot(correlationLogScores)
plt.xlabel("Cases offset (days)")
plt.ylabel("Norm of correlation vector")
plt.title("Logarithmic correlation vs. data offset")
plt.show()
sns.pairplot(bestLinearData[[
'newAndPnew', 'retail_and_recreation_percent_change_from_baseline',
'grocery_and_pharmacy_percent_change_from_baseline',
'parks_percent_change_from_baseline',
'workplaces_percent_change_from_baseline',
'residential_percent_change_from_baseline', 'originalCases'
]],
diag_kind='kde')
plt.show()
sns.pairplot(bestLogData[[
'newAndPnew', 'retail_and_recreation_percent_change_from_baseline',
'grocery_and_pharmacy_percent_change_from_baseline',
'parks_percent_change_from_baseline',
'workplaces_percent_change_from_baseline',
'residential_percent_change_from_baseline', 'originalCases'
]],
diag_kind='kde')
plt.show()
print("Best Full Correlation:", bestLinearCorr)
print("Best Full Correlation Norm:", np.linalg.norm(bestLinearCorr))
print("Best Full Offset:", bestLinearOffset)
print("Best Log Correlation:", bestLogCorr)
print("Best Log Correlation Norm:", np.linalg.norm(bestLogCorr))
print("Best Log Offset:", bestLogOffset)
#Define models
normalizer = preprocessing.Normalization()
caseNormalizer = preprocessing.Normalization()
linear_model = tf.keras.Sequential([normalizer, layers.Dense(units=1)])
linear_model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.1),
loss='mean_absolute_error')
dnn_model = keras.Sequential([
normalizer,
layers.Dense(64, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(1)
])
dnn_model.compile(loss='mean_absolute_error',
optimizer=tf.keras.optimizers.Adam(0.001))
cases_model = tf.keras.Sequential([
normalizer,
layers.Dense(64, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(1)
])
cases_model.compile(loss='mean_absolute_error',
optimizer=tf.keras.optimizers.Adam(0.001))
linearMSE = []
logMSEAdj = []
linearDNNMSE = []
logDNNMSEAdj = []
linearCasesMSE = []
logCasesMSE = []
#Convert data to numpy
linearCasesOnly = bestLinearData['originalCases'].to_numpy()
logCasesOnly = np.log(linearCasesOnly)
bestLinearData = bestLinearData.to_numpy()
bestLogData = bestLogData.to_numpy()
stride = 10 #trains a new model every {stride} days
maxEpoch = 100
for t in range(
(min(bestLinearData.shape[0], bestLogData.shape[0]) - 60) // stride):
print("Training model:", t)
linearTrainX = bestLinearData[t * stride:t * stride + 30, 1:]
linearTrainy = bestLinearData[t * stride:t * stride + 30, :1]
logTrainy2 = np.log(linearTrainy + 1)
logTrainX = bestLogData[t * stride:t * stride + 30, 1:]
logTrainy = bestLogData[t * stride:t * stride + 30, :1]
linearCasesTrainX = linearCasesOnly[t * stride:t * stride + 30]
logCasesTrainX = logCasesOnly[t * stride:t * stride + 30]
linearTestX = bestLinearData[t * stride + 30:t * stride + 60, 1:]
linearTesty = bestLinearData[t * stride + 30:t * stride + 60, :1]
logTestX = bestLogData[t * stride + 30:t * stride + 60, 1:]
logTesty = bestLogData[t * stride + 30:t * stride + 60, :1]
linearCasesTestX = linearCasesOnly[t * stride + 30:t * stride + 60]
logCasesTestX = logCasesOnly[t * stride + 30:t * stride + 60]
#fit linear model
linHistory = linear_model.fit(linearTrainX,
linearTrainy,
epochs=maxEpoch,
verbose=0)
evaluate = linear_model.evaluate(linearTestX, linearTesty, verbose=0)
predict = linear_model.predict(linearTestX, verbose=0)
linearMSE.append(np.abs(predict - linearTesty) / linearTesty)
reset_weights(linear_model)
#fit log model
logHistory = linear_model.fit(logTrainX,
logTrainy,
epochs=maxEpoch,
verbose=0)
evaluate = linear_model.evaluate(logTestX, logTesty, verbose=0)
predict = linear_model.predict(logTestX, verbose=0)
predictAdj = np.exp(predict) - 1 + np.min(
full_dataframe_noDate.to_numpy(
)) #convert from log back to raw case number
logMSEAdj.append(np.abs(predictAdj - linearTesty) / linearTesty)
reset_weights(linear_model)
#fit linear DNN model
linHistory = dnn_model.fit(linearTrainX,
linearTrainy,
epochs=maxEpoch,
verbose=0)
evaluate = dnn_model.evaluate(linearTestX, linearTesty, verbose=0)
predict = dnn_model.predict(linearTestX, verbose=0)
linearDNNMSE.append(np.abs(predict - linearTesty) / linearTesty)
reset_weights(dnn_model)
#fit log DNN model
logHistory = dnn_model.fit(logTrainX,
logTrainy,
epochs=maxEpoch,
verbose=0)
evaluate = dnn_model.evaluate(logTestX, logTesty, verbose=0)
predict = dnn_model.predict(logTestX, verbose=0)
predictAdj = np.exp(predict) - 1 + np.min(
full_dataframe_noDate.to_numpy(
)) #convert from log back to raw case number
#print(predictAdj-linearTesty)
logDNNMSEAdj.append(np.abs(predictAdj - linearTesty) / linearTesty)
reset_weights(dnn_model)
#fit linear cases only model
linHistory = cases_model.fit(linearCasesTrainX,
linearTrainy,
epochs=maxEpoch,
verbose=0)
evaluate = cases_model.evaluate(linearCasesTestX,
linearTesty,
verbose=0)
predict = cases_model.predict(linearCasesTestX, verbose=0)
linearCasesMSE.append(np.abs(predict - linearTesty) / linearTesty)
if showPlot:
visualize_cases(cases_model, linearCasesTrainX, linearTrainy,
linearCasesTestX, linearTesty)
reset_weights(cases_model)
#fit log cases only model
linHistory = cases_model.fit(logCasesTrainX,
logTrainy2,
epochs=maxEpoch,
verbose=0)
evaluate = cases_model.evaluate(logCasesTestX, logTesty, verbose=0)
predict = cases_model.predict(logCasesTestX, verbose=0)
predictAdj = np.exp(
predict) - 1 #convert from log back to raw case number
logCasesMSE.append(np.abs(predictAdj - linearTesty) / linearTesty)
if showPlot:
visualize_cases(cases_model, logCasesTrainX, logTrainy2,
logCasesTestX, logTesty)
reset_weights(cases_model)
plt.plot(np.array(linearMSE).mean(axis=0), label='Linear')
plt.plot(np.array(logMSEAdj).mean(axis=0), label='Log Adjusted')
plt.plot(np.array(linearDNNMSE).mean(axis=0), label='Linear DNN')
plt.plot(np.array(logDNNMSEAdj).mean(axis=0), label='Log DNN Adjusted')
plt.plot(np.array(linearCasesMSE).mean(axis=0), label='Linear Cases')
plt.plot(np.array(logCasesMSE).mean(axis=0), label='Log Cases')
plt.legend(loc="upper left")
plt.show()
def build_train(self):
AUTOTUNE = self.AUTOTUNE
spectrogram_ds = self.spectrogram_ds
commands = self.commands
# repeat the training set preprocessing on the validation and test sets.
train_ds = spectrogram_ds
val_ds = self.preprocess_dataset(self.val_files)
test_ds = self.preprocess_dataset(self.test_files)
# Batch the training and validation sets for model training.
batch_size = 64
train_ds = train_ds.batch(batch_size)
val_ds = val_ds.batch(batch_size)
# Add dataset cache() and prefetch() operations to reduce read latency while training the model.
train_ds = train_ds.cache().prefetch(AUTOTUNE)
val_ds = val_ds.cache().prefetch(AUTOTUNE)
self.test_ds = test_ds
if not os.path.exists("speech.model"):
for spectrogram, _ in spectrogram_ds.take(1):
input_shape = spectrogram.shape
print('Input shape:', input_shape)
num_labels = len(commands)
norm_layer = preprocessing.Normalization()
norm_layer.adapt(spectrogram_ds.map(lambda x, _: x))
model = models.Sequential([
layers.Input(shape=input_shape),
preprocessing.Resizing(32, 32),
norm_layer,
layers.Conv2D(32, 3, activation='relu'),
layers.Conv2D(64, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Dropout(0.25),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dropout(0.5),
layers.Dense(num_labels),
])
model.summary()
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True),
metrics=['accuracy'],
)
EPOCHS = 10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=EPOCHS,
callbacks=tf.keras.callbacks.EarlyStopping(verbose=1,
patience=2),
)
metrics = history.history
fig4 = plt.figure()
plt.plot(history.epoch, metrics['loss'], metrics['val_loss'])
plt.legend(['loss', 'val_loss'])
model.save('speech.model')
def normalization(spectrogram_ds):
norm_layer = preprocessing.Normalization()
norm_layer.adapt(spectrogram_ds.map(lambda x, _: x))
return norm_layer
def train_model(directory):
file_names, accel, gyro, barometer, pedometer, data = read_data(directory)
accelMeanList = list()
gyroMeanList = list()
speedList = list()
timestampsList = list()
df = pd.DataFrame(columns=["speed", "accel", "gyro", "time"])
# df = pd.DataFrame(
# {
# "speed":[0],
# "accel":[0],
# "gyro":[0]
# }
# )
for i in range(len(accel)):
accelMean = pd.DataFrame()
accelMean.insert(loc=0, column=0, value=accel[i].iloc[:, 0])
accelMean.insert(
loc=1,
column=1,
value=np.sqrt(accel[i].iloc[:, 1]**2 + accel[i].iloc[:, 2]**2 +
accel[i].iloc[:, 3]**2))
accelMean = (accelMean.iloc[:, 1].groupby(accelMean.iloc[:, 0]).mean())
accelMean = (accelMean.reindex(range(accelMean.index.max() + 1)))
accelMean = (accelMean.fillna(0)).values
gyroMean = pd.DataFrame()
gyroMean.insert(loc=0, column=0, value=gyro[i].iloc[:, 0])
gyroMean.insert(
loc=1,
column=1,
value=np.sqrt(gyro[i].iloc[:, 1]**2 + gyro[i].iloc[:, 2]**2 +
gyro[i].iloc[:, 3]**2))
gyroMean = (gyroMean.iloc[:, 1].groupby(gyroMean.iloc[:, 0]).mean())
gyroMean = (gyroMean.reindex(range(gyroMean.index.max() + 1)))
gyroMean = (gyroMean.fillna(0)).values
steps = pd.DataFrame(pedometer[i].iloc[:, 1].groupby(
pedometer[i].iloc[:, 0]).sum())
steps = steps.reindex(range(steps.index.max() + 1))
steps = (steps.fillna(0))
stride = (float)(data[i].iloc[0, 0])
speed = steps.iloc[:, 0].values * stride
speedList.append(speed)
# timestamps = range(len(accelMean) + 1)
timestampsList.append(range(len(accelMean) + 1))
dfi = pd.DataFrame({
"speed": speed,
"accel": accelMean,
"gyro": gyroMean
})
dfi = dfi.iloc[1:]
dfi["time"] = len(dfi.index)
df = pd.concat([df, dfi])
df = df.sample(frac=1).reset_index(drop=True)
train_size = round((len(df) / 100) * 80)
train_dataset = df.sample(frac=0.8, random_state=0)
test_dataset = df.drop(train_dataset.index)
x_train = train_dataset.copy()
x_test = test_dataset.copy()
y_train = x_train.pop("time")
y_test = x_test.pop("time")
x_train = np.asarray(x_train).astype('float32')
x_test = np.asarray(x_test).astype('float32')
y_train = np.asarray(y_train).astype('float32')
y_test = np.asarray(y_test).astype('float32')
# print(train_dataset.describe().transpose()[['mean', 'std']])
normalizer = preprocessing.Normalization()
normalizer.adapt(np.array(x_train))
linear_model = tf.keras.Sequential([normalizer, layers.Dense(units=1)])
linear_model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.1),
loss='mean_absolute_error')
linear_model.fit(
x_train,
y_train,
epochs=100,
verbose=0,
validation_data=(x_test, y_test),
)
print(x_test[:1])
print(linear_model.predict(x_test[:1]))
# tf.saved_model.save(linear_model, "")
# linear_model.save("mnist.h5")
converter = tf.compat.v2.lite.TFLiteConverter.from_keras_model(
linear_model)
tflite_model = converter.convert()
open('linear.tflite', 'wb').write(tflite_model)
def main():
# In memory data
url = 'https://storage.googleapis.com/download.tensorflow.org/data/abalone_train.csv'
abalone_train = pd.read_csv(url,
names=[
'Length', 'Diamenter', 'Height',
'Whole weight', 'Viscera weight',
'Shell weight', 'Age'
])
print(abalone_train.head())
abalone_features = abalone_train.copy()
abalone_labels = abalone_features.pop('Age')
abalone_features = np.array(abalone_features)
print(f'Features: {abalone_features}')
abalone_model = tf.keras.Sequential([layers.Dense(64), layers.Dense(1)])
abalone_model.compile(loss=tf.losses.MeanSquaredError(),
optimizer=tf.optimizers.Adam())
# Basic preprocessing
normalize = preprocessing.Normalization()
normalize.adapt(abalone_features)
norm_abalone_model = tf.keras.Sequential(
[normalize, layers.Dense(64),
layers.Dense(1)])
norm_abalone_model.compile(loss=tf.losses.MeanSquaredError(),
optimizer=tf.optimizers.Adam())
norm_abalone_model.fit(abalone_features, abalone_labels, epochs=10)
# Mixed data types
url = 'https://storage.googleapis.com/tf-datasets/titanic/train.csv'
titanic = pd.read_csv(url)
print(titanic.head())
titanic_features = titanic.copy()
titanic_labels = titanic_features.pop('survived')
# Create a symbolic input
input = tf.keras.Input(shape=(), dtype=tf.float32)
# Do a calculation using is
result = 2 * input + 1
# The result doesn't have a value
print(f'Result: {result}')
calc = tf.keras.Model(inputs=input, outputs=result)
print(f'calc(1) = {calc(1).numpy()}')
print(f'calc(2) = {calc(2).numpy()}')
inputs = {}
for name, column in titanic_features.items():
dtype = column.dtype
if dtype == object:
dtype = tf.string
else:
dtype = tf.float32
inputs[name] = tf.keras.Input(shape=(1, ), name=name, dtype=dtype)
inputs
numeric_inputs = {
name: input
for name, input in inputs.items() if input.dtype == tf.float32
}
x = layers.Concatenate()(list(numeric_inputs.values()))
norm = preprocessing.Normalization()
norm.adapt(np.array(titanic[numeric_inputs.keys()]))
all_numeric_inputs = norm(x)
all_numeric_inputs
preprocessed_inputs = [all_numeric_inputs]
for name, input in inputs.items():
if input.dtype == tf.float32:
continue
lookup = preprocessing.StringLookup(
vocabulary=np.unique(titanic_features[name]))
one_hot = preprocessing.CategoryEncoding(
max_tokens=lookup.vocab_size())
x = lookup(input)
x = one_hot(x)
preprocessed_inputs.append(x)
preprocessed_inputs_cat = layers.Concatenate()(preprocessed_inputs)
titanic_preprocessing = tf.keras.Model(inputs, preprocessed_inputs_cat)
tf.keras.utils.plot_model(model=titanic_preprocessing,
rankdir='LR',
dpi=72,
show_shapes=True)
titanic_features_dict = {
name: np.array(value)
for name, value in titanic_features.items()
}
features_dict = {
name: values[:1]
for name, values in titanic_features_dict.items()
}
titanic_preprocessing(features_dict)
titanic_model = get_titanic_model(titanic_preprocessing, inputs)
titanic_model.fit(x=titanic_features_dict, y=titanic_labels, epochs=10)
titanic_model.save('test')
reloaded = tf.keras.models.load_model('test')
features_dict = {
name: values[:1]
for name, values in titanic_features_dict.items()
}
before = titanic_model(features_dict)
after = reloaded(features_dict)
assert (before - after) < 1e-3
print(f'Before: {before}')
print(f'After: {after}')
# Using tf.data
# On in memory datasets
for example in slices(titanic_features_dict):
for name, value in example.items():
print(f'{name:19s}: {value}')
break
titanic_ds = tf.data.Dataset.from_tensor_slices(
(titanic_features_dict, titanic_labels))
titanic_batches = titanic_ds.shuffle(len(titanic_labels)).batch(32)
titanic_model.fit(titanic_batches, epochs=5)
# From a single file
url = 'https://storage.googleapis.com/tf-datasets/titanic/train.csv'
titanic_file_path = tf.keras.utils.get_file('train.csv', url)
titanic_csv_ds = tf.data.experimental.make_csv_dataset(
titanic_file_path,
batch_size=5, # Artificiallly small to make examples easier to show.
label_name='survived',
num_epochs=1,
ignore_errors=True,
)
for batch, label in titanic_csv_ds.take(1):
for key, value in batch.items():
print(f'{key:20s}: value')
print()
print(f'{"label":20s}: {label}')
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00492/Metro_Interstate_Traffic_Volume.csv.gz'
traffic_volume_csv_gz = tf.keras.utils.get_file(
'Metro_Interstate_Traffic_Volume.csv.gz',
url,
cache_dir='.',
cache_subdir='traffic')
traffic_volume_csv_gz_ds = tf.data.experimental.make_csv_dataset(
traffic_volume_csv_gz,
batch_size=256,
label_name='traffic_volume',
num_epochs=1,
compression_type='GZIP')
for batch, label in traffic_volume_csv_gz_ds.take(1):
for key, value in batch.items():
print(f'{key:20s}: {value[:5]}')
print()
print(f'{"label":20s}: {label[:5]}')
#Caching
start = time.time()
for i, (batch, label) in enumerate(traffic_volume_csv_gz_ds.repeat(20)):
if i % 40 == 0:
print('.', end='')
print(f'Total time: {time.time() - start:.3f}')
caching = traffic_volume_csv_gz_ds.cache().shuffle(1000)
start = time.time()
for i, (batch, label) in enumerate(caching.shuffle(1000).repeat(20)):
if i % 40 == 0:
print('.', end='')
print(f'Total time: {time.time() - start:.3f}')
start = time.time()
snapshot = tf.data.experimental.snapshot('titanic.tfsnap')
snapshotting = traffic_volume_csv_gz_ds.apply(snapshot).shuffle(1000)
for i, (batch, label) in enumerate(snapshotting.shuffle(1000).repeat(20)):
if i % 40 == 0:
print('.', end='')
print(f'Total time: {time.time() - start:.3f}')
# Multiple files
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00417/fonts.zip'
_ = tf.keras.utils.get_file('fonts.zip',
url,
cache_dir='.',
cache_subdir='fonts',
extract=True)
fonts_csvs = sorted(str(p) for p in pathlib.Path('fonts').glob('*.csv'))
print(f'Fonts: {fonts_csvs[:10]}')
print(f'Fonts len: {len(fonts_csvs)}')
fonts_ds = tf.data.experimental.make_csv_dataset(
file_pattern='fonts/*.csv',
batch_size=10,
num_epochs=1,
num_parallel_reads=20,
shuffle_buffer_size=10000)
for features in fonts_ds.take(1):
for i, (name, value) in enumerate(features.items()):
if i > 15:
break
print(f'{name:20s}: {value}')
print('...')
print(f'[total: {len(features)} features]')
# Optional: Packing fields
fonts_image_ds = fonts_ds.map(make_images)
for features in fonts_image_ds.take(1):
break
plt.figure(figsize=(6, 6), dpi=120)
for n in range(9):
plt.subplot(3, 3, n + 1)
plt.imshow(features['image'][..., n])
plt.title(chr(features['m_label'][n]))
plt.axis('off')
plt.show()
# Lower level functions
# `tf.io.decode_csv`
text = pathlib.Path(titanic_file_path).read_text()
lines = text.split('\n')[1:-1]
all_strings = [str()] * 10
print(f'{all_strings}')
features = tf.io.decode_csv(lines, record_defaults=all_strings)
for f in features:
print(f'type: {f.dtype.name}, shape: {f.shape}')
print(f'Sample record: {lines[0]}')
titanic_types = [
int(),
str(),
float(),
int(),
int(),
float(),
str(),
str(),
str(),
str()
]
print(f'Data types: {titanic_types}')
features = tf.io.decode_csv(lines, record_defaults=titanic_types)
for f in features:
print(f'type: {f.dtype.name}, shape: {f.shape}')
# `tf.data.experimental.CsvDataset`
simple_titanic = tf.data.experimental.CsvDataset(
titanic_file_path, record_defaults=titanic_types, header=True)
for example in simple_titanic.take(1):
print(f'Sample record: {[e.numpy() for e in example]}')
def decode_titanic_line(line):
return tf.io.decode_csv(line, titanic_types)
manual_titanic = (
# Load the lines of text
tf.data.TextLineDataset(titanic_file_path)
# Skip the header row
.skip(1)
# Decode the line
.map(decode_titanic_line))
for example in manual_titanic.take(1):
print(f'Sample record: {[e.numpy() for e in example]}')
# Multiple files
font_line = pathlib.Path(fonts_csvs[0]).read_text().splitlines()[1]
print(f'Sample: {font_line}')
num_font_features = font_line.count(',') + 1
font_column_types = [str(), str()] + [float()] * (num_font_features - 2)
print(f'Fonts[0]: {fonts_csvs[0]}')
simple_font_ds = tf.data.experimental.CsvDataset(
fonts_csvs, record_defaults=font_column_types, header=True)
for row in simple_font_ds.take(10):
print(f'CSV first column: {row[0].numpy()}')
font_files = tf.data.Dataset.list_files('fonts/*.csv')
print('Epoch 1:')
for f in list(font_files)[:5]:
print(f' {f.numpy()}')
print(' ...')
print()
print('Epoch 2:')
for f in list(font_files)[:5]:
print(f' {f.numpy()}')
print(' ...')
def make_font_csv_ds(path):
return tf.data.experimental.CsvDataset(
path, record_defaults=font_column_types, header=True)
font_rows = font_files.interleave(make_font_csv_ds, cycle_length=3)
fonts_dict = {'font_name': [], 'character': []}
for row in font_rows.take(10):
fonts_dict['font_name'].append(row[0].numpy().decode())
fonts_dict['character'].append(chr(row[2].numpy()))
print(pd.DataFrame(fonts_dict))
# Performance
BATCH_SIZE = 2048
font_ds = tf.data.experimental.make_csv_dataset(file_pattern='fonts/*.csv',
batch_size=BATCH_SIZE,
num_epochs=1,
num_parallel_reads=100)
start = time.time()
for i, batch in enumerate(font_ds.take(20)):
print('.', end='')
print(f'Total time: {time.time() - start:.3f}')
sns.pairplot(train_dataset[[
'Energia', 'Corrente', 'Voltaggio', 'ActivePower', 'Response', 'Waste'
]],
diag_kind='kde').savefig("Response.png")
print(train_dataset.describe().transpose())
train_features = train_dataset.copy()
test_features = test_dataset.copy()
print(train_dataset.describe().transpose()[['mean', 'std']])
train_labels = train_features.pop('Corrente')
test_labels = test_features.pop('Corrente')
normalizer = preprocessing.Normalization()
normalizer.adapt(np.array(train_features))
print(normalizer.mean.numpy())
first = np.array(train_features[:1])
with np.printoptions(precision=2, suppress=True):
print('First example:', first)
print()
print('Normalized:', normalizer(first).numpy())
horsepower = np.array(train_features['Waste'])
horsepower_normalizer = preprocessing.Normalization(input_shape=[
1,
def NeuralNetModel(x_train, y_train, x_val, y_val, params):
"""
Keras model for the Neural Network, used to scan the hyperparameter space by Talos
Uses the data provided as inputs
"""
# Split y = [target,weight], Talos does not leave room for the weight so had to be included in one of the arrays
w_train = y_train[:, -1]
w_val = y_val[:, -1]
y_train = y_train[:, :-1]
y_val = y_val[:, :-1]
x_train_lbn = x_train[:, -len(parameters.LBN_inputs):].reshape(
-1, 4,
len(parameters.LBN_inputs) // 4)
x_train = x_train[:, :-len(parameters.LBN_inputs)]
x_val_lbn = x_val[:, -len(parameters.LBN_inputs):].reshape(
-1, 4,
len(parameters.LBN_inputs) // 4)
x_val = x_val[:, :-len(parameters.LBN_inputs)]
# Scaler #
with open(parameters.scaler_path,
'rb') as handle: # Import scaler that was created before
scaler = pickle.load(handle)
# Design network #
# Left branch : classic inputs -> Preprocess -> onehot
inputs_numeric = []
means = []
variances = []
inputs_all = []
encoded_all = []
for idx in range(x_train.shape[1]):
inpName = parameters.inputs[idx].replace('$', '')
input_layer = tf.keras.Input(shape=(1, ), name=inpName)
# Categorical inputs #
if parameters.mask_op[idx]:
operation = getattr(Operations, parameters.operations[idx])()
encoded_all.append(operation(input_layer))
# Numerical inputs #
else:
inputs_numeric.append(input_layer)
means.append(scaler.mean_[idx])
variances.append(scaler.var_[idx])
inputs_all.append(input_layer)
# Concatenate all numerical inputs #
if int(tf_version[1]) < 4:
normalizer = preprocessing.Normalization(name='Normalization')
x_dummy = np.ones((10, len(means)))
# Needs a dummy to call the adapt method before setting the weights
normalizer.adapt(x_dummy)
normalizer.set_weights([np.array(means), np.array(variances)])
else:
normalizer = preprocessing.Normalization(mean=means,
variance=variances,
name='Normalization')
encoded_all.append(
normalizer(tf.keras.layers.concatenate(inputs_numeric,
name='Numerics')))
if len(encoded_all) > 1:
all_features = tf.keras.layers.concatenate(encoded_all,
axis=-1,
name="Features")
else:
all_features = encoded_all[0]
# Right branch : LBN
input_lbn_Layer = Input(shape=x_train_lbn.shape[1:], name='LBN_inputs')
lbn_layer = LBNLayer(
x_train_lbn.shape[1:],
n_particles=max(params['n_particles'],
1), # Hack so that 0 does not trigger error
boost_mode=LBN.PAIRS,
features=["E", "px", "py", "pz", "pt", "p", "m", "pair_cos"],
name='LBN')(input_lbn_Layer)
batchnorm = tf.keras.layers.BatchNormalization(name='batchnorm')(lbn_layer)
# Concatenation of left and right #
concatenate = tf.keras.layers.Concatenate(axis=-1)(
[all_features, batchnorm])
L1 = Dense(params['first_neuron'],
activation=params['activation'],
kernel_regularizer=l2(params['l2']))(
concatenate if params['n_particles'] > 0 else all_features)
hidden = hidden_layers(params, 1, batch_normalization=True).API(L1)
out = Dense(y_train.shape[1],
activation=params['output_activation'],
name='out')(hidden)
# Check preprocessing #
preprocess = Model(inputs=inputs_numeric, outputs=encoded_all[-1])
x_numeric = x_train[:, [not m for m in parameters.mask_op]]
out_preprocess = preprocess.predict(np.hsplit(x_numeric,
x_numeric.shape[1]),
batch_size=params['batch_size'])
mean_scale = np.mean(out_preprocess)
std_scale = np.std(out_preprocess)
if abs(mean_scale) > 0.01 or abs(
(std_scale - 1) /
std_scale) > 0.1: # Check that scaling is correct to 1%
logging.warning(
"Something is wrong with the preprocessing layer (mean = %0.6f, std = %0.6f), maybe you loaded an incorrect scaler"
% (mean_scale, std_scale))
# Tensorboard logs #
#path_board = os.path.join(parameters.main_path,"TensorBoard")
#suffix = 0
#while(os.path.exists(os.path.join(path_board,"Run_"+str(suffix)))):
# suffix += 1
#path_board = os.path.join(path_board,"Run_"+str(suffix))
#os.makedirs(path_board)
#logging.info("TensorBoard log dir is at %s"%path_board)
# Callbacks #
# Early stopping to stop learning if val_loss plateau for too long #
early_stopping = EarlyStopping(**parameters.early_stopping_params)
# Reduce learnign rate in case of plateau #
reduceLR = ReduceLROnPlateau(**parameters.reduceLR_params)
# Custom loss function plot for debugging #
loss_history = LossHistory()
# Tensorboard for checking live the loss curve #
#board = TensorBoard(log_dir=path_board,
# histogram_freq=1,
# batch_size=params['batch_size'],
# write_graph=True,
# write_grads=True,
# write_images=True)
Callback_list = [loss_history, early_stopping, reduceLR]
# Compile #
if 'resume' not in params: # Normal learning
# Define model #
model_inputs = [inputs_all]
if params['n_particles'] > 0:
model_inputs.append(input_lbn_Layer)
model = Model(inputs=model_inputs, outputs=[out])
initial_epoch = 0
else: # a model has to be imported and resumes training
#custom_objects = {'PreprocessLayer': PreprocessLayer,'OneHot': OneHot.OneHot}
logging.info("Loaded model %s" % params['resume'])
a = Restore(params['resume'],
custom_objects=custom_objects,
method='h5')
model = a.model
initial_epoch = params['initial_epoch']
model.compile(optimizer=Adam(lr=params['lr']),
loss=params['loss_function'],
metrics=[
tf.keras.metrics.CategoricalAccuracy(),
tf.keras.metrics.AUC(multi_label=True),
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall()
])
model.summary()
fit_inputs = np.hsplit(x_train, x_train.shape[1])
fit_val = (np.hsplit(x_val, x_val.shape[1]), y_val, w_val)
if params['n_particles'] > 0:
fit_inputs.append(x_train_lbn)
fit_val[0].append(x_val_lbn)
# Fit #
history = model.fit(x=fit_inputs,
y=y_train,
sample_weight=w_train,
epochs=params['epochs'],
batch_size=params['batch_size'],
verbose=1,
validation_data=fit_val,
callbacks=Callback_list)
# Plot history #
PlotHistory(loss_history, params)
return history, model
true_k = 1.3 # slope
true_d = 0.3 # intercept
NUM_EXAMPLES = 100
X = tf.random.normal(shape=(NUM_EXAMPLES, ))
noise = tf.random.normal(shape=(NUM_EXAMPLES, ))
y = X * true_k + true_d + noise
train_x = np.array(X)
# Register TensorBoard tracing callback
tensorboard_callback = keras.callbacks.TensorBoard(log_dir='./logs')
# We use a single-variable linear regression, to predict the y values from a given X values.
reg_normalizer = preprocessing.Normalization(input_shape=[
1,
])
reg_normalizer.adapt(train_x)
regression_model = tf.keras.Sequential([reg_normalizer, layers.Dense(units=1)])
regression_model.summary()
print(regression_model.predict(X[:10]))
regression_model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.1),
loss='mean_absolute_error')
history = regression_model.fit(
X,
y,
def NeuralNetGeneratorModel(x_train, y_train, x_val, y_val, params):
"""
Keras model for the Neural Network, used to scan the hyperparameter space by Talos
Uses the generator rather than the input data (which are dummies)
"""
# Scaler #
with open(parameters.scaler_path,
'rb') as handle: # Import scaler that was created before
scaler = pickle.load(handle)
# Design network #
# Left branch : classic inputs -> Preprocess -> onehot
inputs_numeric = []
means = []
variances = []
inputs_all = []
encoded_all = []
for idx in range(x_train.shape[1]):
inpName = parameters.inputs[idx].replace('$', '').replace(' ',
'').replace(
'_', '')
input_layer = tf.keras.Input(shape=(1, ), name=inpName)
# Categorical inputs #
if parameters.mask_op[idx]:
operation = getattr(Operations, parameters.operations[idx])()
encoded_all.append(operation(input_layer))
# Numerical inputs #
else:
inputs_numeric.append(input_layer)
means.append(scaler.mean_[idx])
variances.append(scaler.var_[idx])
inputs_all.append(input_layer)
# Concatenate all numerical inputs #
if int(tf_version[1]) < 4:
normalizer = preprocessing.Normalization(name='Normalization')
x_dummy = np.ones((10, len(means)))
# Needs a dummy to call the adapt method before setting the weights
normalizer.adapt(x_dummy)
normalizer.set_weights([np.array(means), np.array(variances)])
else:
normalizer = preprocessing.Normalization(mean=means,
variance=variances,
name='Normalization')
encoded_all.append(
normalizer(tf.keras.layers.concatenate(inputs_numeric,
name='Numerics')))
if len(encoded_all) > 1:
all_features = tf.keras.layers.concatenate(encoded_all,
axis=-1,
name="Features")
else:
all_features = encoded_all[0]
# Right branch : LBN
lbn_input_shape = (len(parameters.LBN_inputs) // 4, 4)
input_lbn_Layer = Input(shape=lbn_input_shape, name='LBN_inputs')
lbn_layer = LBNLayer(
lbn_input_shape,
n_particles=max(params['n_particles'],
1), # Hack so that 0 does not trigger error
boost_mode=LBN.PAIRS,
features=["E", "px", "py", "pz", "pt", "p", "m", "pair_cos"],
name='LBN')(input_lbn_Layer)
batchnorm = tf.keras.layers.BatchNormalization(name='batchnorm')(lbn_layer)
# Concatenation of left and right #
concatenate = tf.keras.layers.Concatenate(axis=-1)(
[all_features, batchnorm])
L1 = Dense(params['first_neuron'],
activation=params['activation'],
kernel_regularizer=l2(params['l2']))(
concatenate if params['n_particles'] > 0 else all_features)
hidden = hidden_layers(params, 1, batch_normalization=True).API(L1)
out = Dense(y_train.shape[1],
activation=params['output_activation'],
name='out')(hidden)
# Tensorboard logs #
# path_board = os.path.join(parameters.main_path,"TensorBoard")
# suffix = 0
# while(os.path.exists(os.path.join(path_board,"Run_"+str(suffix)))):
# suffix += 1
# path_board = os.path.join(path_board,"Run_"+str(suffix))
# os.makedirs(path_board)
# logging.info("TensorBoard log dir is at %s"%path_board)
# Callbacks #
# Early stopping to stop learning if val_loss plateau for too long #
early_stopping = EarlyStopping(**parameters.early_stopping_params)
# Reduce learnign rate in case of plateau #
reduceLR = ReduceLROnPlateau(**parameters.reduceLR_params)
# Custom loss function plot for debugging #
loss_history = LossHistory()
# Tensorboard for checking live the loss curve #
# board = TensorBoard(log_dir=path_board,
# histogram_freq=1,
# batch_size=params['batch_size'],
# write_graph=True,
# write_grads=True,
# write_images=True)
# Callback_list = [loss_history,early_stopping,reduceLR,board]
Callback_list = [loss_history, early_stopping, reduceLR]
# Compile #
if 'resume' not in params: # Normal learning
# Define model #
model_inputs = [inputs_all]
if params['n_particles'] > 0:
model_inputs.append(input_lbn_Layer)
model = Model(inputs=model_inputs, outputs=[out])
initial_epoch = 0
else: # a model has to be imported and resumes training
#custom_objects = {'PreprocessLayer': PreprocessLayer,'OneHot': OneHot.OneHot}
logging.info("Loaded model %s" % params['resume'])
a = Restore(params['resume'],
custom_objects=custom_objects,
method='h5')
model = a.model
initial_epoch = params['initial_epoch']
model.compile(optimizer=Adam(lr=params['lr']),
loss=params['loss_function'],
metrics=[
tf.keras.metrics.CategoricalAccuracy(),
tf.keras.metrics.AUC(multi_label=True),
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall()
])
model.summary()
# Generator #
training_generator = DataGenerator(
path=parameters.config,
inputs=parameters.inputs,
outputs=parameters.outputs,
inputsLBN=parameters.LBN_inputs if params['n_particles'] > 0 else None,
cut=parameters.cut,
weight=parameters.weight,
batch_size=params['batch_size'],
state_set='training',
model_idx=params['model_idx'] if parameters.crossvalidation else None)
validation_generator = DataGenerator(
path=parameters.config,
inputs=parameters.inputs,
outputs=parameters.outputs,
inputsLBN=parameters.LBN_inputs if params['n_particles'] > 0 else None,
cut=parameters.cut,
weight=parameters.weight,
batch_size=params['batch_size'],
state_set='validation',
model_idx=params['model_idx'] if parameters.crossvalidation else None)
# Some verbose logging #
logging.info("Will use %d workers" % parameters.workers)
logging.warning("Tensorflow location " + tf.__file__)
if len(tf.config.experimental.list_physical_devices('XLA_GPU')) > 0:
logging.info("GPU detected")
#logging.warning(K.tensorflow_backend._get_available_gpus())
# Fit #
history = model.fit_generator(
generator=training_generator, # Training data from generator instance
validation_data=
validation_generator, # Validation data from generator instance
epochs=params['epochs'], # Number of epochs
verbose=1,
max_queue_size=parameters.workers * 2, # Length of batch queue
callbacks=Callback_list, # Callbacks
initial_epoch=
initial_epoch, # In case of resumed training will be different from 0
workers=parameters.
workers, # Number of threads for batch generation (0 : all in same)
shuffle=True, # Shuffle order at each epoch
use_multiprocessing=True) # Needs to be turned on for queuing batches
# Plot history #
PlotHistory(loss_history)
return history, model
compression='gzip')
f.create_dataset('spectr_valid',data=spectr_valid,
compression='gzip')
f.create_dataset('label_valid',data=label_valid,
compression='gzip')
f.create_dataset('spectr_test',data=spectr_test,
compression='gzip')
f.create_dataset('label_test',data=label_test,
compression='gzip')
f.close()
print('file size: %s'%list(os.stat(h5f))[6])
for spectrogram,_ in train_ds.take(1):
input_shape=spectrogram.shape
num_labels=len(names)
norm_layer=tkp.Normalization()
norm_layer.adapt(train_ds.map(lambda x,_:x))
model=tkm.Sequential([
tkl.InputLayer(input_shape=input_shape),
tkp.Resizing(32,32),
norm_layer,
tkl.Conv2D(32,3,activation='relu'),
tkl.Conv2D(96,3,activation='relu'),
tkl.MaxPooling2D(),
tkl.Dropout(.25),
tkl.Flatten(),
tkl.Dense(256,activation='relu'),
tkl.Dropout(.5),
tkl.Dense(num_labels),
])
model.summary()
def run_whole_thing(out_dir):
os.makedirs(out_dir, exist_ok=True)
# Set seed for experiment reproducibility
seed = 55
tf.random.set_seed(seed)
np.random.seed(seed)
data_dir = pathlib.Path("data/mini_speech_commands")
if not data_dir.exists():
# Get the files from external source and put them in an accessible directory
tf.keras.utils.get_file(
'mini_speech_commands.zip',
origin=
"http://storage.googleapis.com/download.tensorflow.org/data/mini_speech_commands.zip",
extract=True)
# Convert the binary audio file to a tensor
def decode_audio(audio_binary):
audio, _ = tf.audio.decode_wav(audio_binary)
return tf.squeeze(audio, axis=-1)
# Get the label (yes, no, up, down, etc) for an audio file.
def get_label(file_path):
parts = tf.strings.split(file_path, os.path.sep)
# Note: You'll use indexing here instead of tuple unpacking to enable this to work in a TensorFlow graph.
return parts[-2]
# Create a tuple that has the labeled audio files
def get_waveform_and_label(file_path):
label = get_label(file_path)
audio_binary = tf.io.read_file(file_path)
waveform = decode_audio(audio_binary)
return waveform, label
# Convert audio files to images
def get_spectrogram(waveform):
# Padding for files with less than 16000 samples
zero_padding = tf.zeros([16000] - tf.shape(waveform), dtype=tf.float32)
# Concatenate audio with padding so that all audio clips will be of the
# same length
waveform = tf.cast(waveform, tf.float32)
equal_length = tf.concat([waveform, zero_padding], 0)
spectrogram = tf.signal.stft(equal_length,
frame_length=255,
frame_step=128)
spectrogram = tf.abs(spectrogram)
return spectrogram
# Label the images created from the audio files and return a tuple
def get_spectrogram_and_label_id(audio, label):
spectrogram = get_spectrogram(audio)
spectrogram = tf.expand_dims(spectrogram, -1)
label_id = tf.argmax(label == commands)
return spectrogram, label_id
# Preprocess any audio files
def preprocess_dataset(files, autotune, commands):
# Creates the dataset
files_ds = tf.data.Dataset.from_tensor_slices(files)
# Matches audio files with correct labels
output_ds = files_ds.map(get_waveform_and_label,
num_parallel_calls=autotune)
# Matches audio file images to the correct labels
output_ds = output_ds.map(get_spectrogram_and_label_id,
num_parallel_calls=autotune)
return output_ds
# Get all of the commands for the audio files
commands = np.array(tf.io.gfile.listdir(str(data_dir)))
commands = commands[commands != 'README.md']
# Get a list of all the files in the directory
filenames = tf.io.gfile.glob(str(data_dir) + '/*/*')
# Shuffle the file names so that random bunches can be used as the training, testing, and validation sets
filenames = tf.random.shuffle(filenames)
# Create the list of files for training data
train_files = filenames[:6400]
# Create the list of files for validation data
validation_files = filenames[6400:6400 + 800]
# Create the list of files for test data
test_files = filenames[-800:]
autotune = tf.data.AUTOTUNE
# Get the converted audio files for training the model
files_ds = tf.data.Dataset.from_tensor_slices(train_files)
waveform_ds = files_ds.map(get_waveform_and_label,
num_parallel_calls=autotune)
spectrogram_ds = waveform_ds.map(get_spectrogram_and_label_id,
num_parallel_calls=autotune)
# Preprocess the training, test, and validation datasets
train_ds = preprocess_dataset(train_files, autotune, commands)
validation_ds = preprocess_dataset(validation_files, autotune, commands)
test_ds = preprocess_dataset(test_files, autotune, commands)
# Batch datasets for training and validation
batch_size = 64
train_ds = train_ds.batch(batch_size)
validation_ds = validation_ds.batch(batch_size)
# Reduce latency while training
train_ds = train_ds.cache().prefetch(autotune)
validation_ds = validation_ds.cache().prefetch(autotune)
# Build model
for spectrogram, _ in spectrogram_ds.take(1):
input_shape = spectrogram.shape
num_labels = len(commands)
norm_layer = preprocessing.Normalization()
norm_layer.adapt(spectrogram_ds.map(lambda x, _: x))
model = models.Sequential([
layers.Input(shape=input_shape),
preprocessing.Resizing(32, 32),
norm_layer,
layers.Conv2D(32, 3, activation='relu'),
layers.Conv2D(64, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Dropout(0.25),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dropout(0.5),
layers.Dense(num_labels),
])
model.summary()
# Configure built model with losses and metrics
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'],
)
# Finally train the model and return info about each epoch
EPOCHS = 10
model.fit(
train_ds,
validation_data=validation_ds,
epochs=EPOCHS,
callbacks=tf.keras.callbacks.EarlyStopping(verbose=1, patience=2),
)
# Test the model
test_audio = []
test_labels = []
for audio, label in test_ds:
test_audio.append(audio.numpy())
test_labels.append(label.numpy())
test_audio = np.array(test_audio)
test_labels = np.array(test_labels)
# See how accurate the model is when making predictions on the test dataset
y_pred = np.argmax(model.predict(test_audio), axis=1)
y_true = test_labels
test_acc = sum(y_pred == y_true) / len(y_true)
print(f'Test set accuracy: {test_acc:.0%}')
print(y_train)
"""## Training the model
### Linear regression model
"""
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.layers import InputLayer
print(tf.__version__)
"""Normalisation layer"""
normalizer = preprocessing.Normalization()
normalizer.adapt(np.array(X_train))
print(normalizer.mean.numpy())
def plot_loss(history):
plt.plot(history.history['loss'], label='loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.ylim([0, 3500])
plt.xlabel('Epoch')
plt.ylabel('Error [Shares]')
plt.legend()
plt.grid(True)
import collections
import inspect
import numpy as np
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.python.util import nest
CombinerPreprocessingLayer = inspect.getmro(preprocessing.Normalization)[1]
Combiner = inspect.getmro(preprocessing.Normalization()._combiner.__class__)[1]
INT = 'int'
NONE = 'none'
ONE_HOT = 'one-hot'
class CategoricalEncoding(CombinerPreprocessingLayer):
"""Encode the categorical features to numerical features.
# Arguments
encoding: A list of strings, which has the same number of elements as the
columns in the structured data. Each of the strings specifies the
encoding method used for the corresponding column. Use 'int' for
categorical columns and 'none' for numerical columns.
"""
# TODO: Support one-hot encoding.
def __init__(self, encoding, **kwargs):
super().__init__(combiner=CategoricalEncodingCombiner(encoding),