def test_loss_with_sample_weight_in_layer_call(self):
class MyLayer(layers.Layer):
def __init__(self):
super(MyLayer, self).__init__()
self.bias = testing_utils.Bias()
def call(self, inputs):
out = self.bias(inputs[0])
self.add_loss(MAE()(inputs[1], out, inputs[2]))
self.add_loss(
math_ops.reduce_mean(inputs[2] * mae(inputs[1], out)))
return out
inputs = Input(shape=(1, ))
targets = Input(shape=(1, ))
sw = Input(shape=(1, ))
outputs = MyLayer()([inputs, targets, sw])
model = Model([inputs, targets, sw], outputs)
model.predict([self.x, self.y, self.w])
model.compile(optimizer_v2.gradient_descent.SGD(0.05),
run_eagerly=testing_utils.should_run_eagerly(),
experimental_run_tf_function=testing_utils.
should_run_tf_function())
history = model.fit([self.x, self.y, self.w], batch_size=3, epochs=5)
self.assertAllClose(history.history['loss'], [2., 1.8, 1.6, 1.4, 1.2],
1e-3)
output = model.evaluate([self.x, self.y, self.w])
self.assertAlmostEqual(output, 1.0, 3)
output = model.test_on_batch([self.x, self.y, self.w])
self.assertAlmostEqual(output, 1.0, 3)
def __init__(self, model_name: str, model: Model,
fs_seq: FileSystemSequence, epochs: int, batch_size: int,
tuned_model_params: Dict[str,
float], model_params: Dict[str,
float],
history: Dict, discovery_path: str, weight_path: str):
self.model_name = model_name
self.layer_shapes: List[LayerStats] = seq(model.layers).map(
lambda layer: LayerStats(layer.name, layer.input_shape, layer.output_shape)) \
.to_list()
fs_seq.set_dataset_type(DatasetType.TEST)
test_eval = model.evaluate(fs_seq)
fs_seq.set_dataset_type(DatasetType.TRAINING)
self.test_loss = test_eval[0].item()
self.test_categorical_accuracy = test_eval[1].item()
self.train_shape = fs_seq.get_train_feature_shape()
self.cross_val_shape = fs_seq.get_crossval_feature_shape()
self.test_shape = fs_seq.get_test_feature_shape()
self.epochs = epochs
self.batch_size = batch_size
self.tuned_model_params = tuned_model_params
self.model_params = model_params
self.history = {
k: seq(history[k]).map(lambda v: v.item()).to_list()
for k in history.keys()
}
self.discovery_path = discovery_path
self.timestamp = datetime.now().timestamp()
self.weight_path = weight_path
def validate_model(model: Model) -> [float, float, float, float]:
y_validate = to_categorical(globals()['VALIDATION_LABELS'])
print(y_validate.shape)
loss, acc, recall, precision = model.evaluate(x=globals()['VALIDATION_DATA'], y=y_validate, batch_size=32)
print("loss: %.4f" % loss)
print("categorical accuracy: %.4f" % acc)
print("recall: %.4f" % recall)
print("precision: %.4f" % precision)
return [loss, acc, recall, precision]
def validate_model_for_all_classes(model: Model) -> dict:
class_labels = dict()
class_entities = dict()
results = dict()
for i in range(0, len(globals()['VALIDATION_LABELS'])):
if globals()['VALIDATION_LABELS'][i] not in class_labels.keys():
class_labels[globals()['VALIDATION_LABELS'][i]] = list()
class_entities[globals()['VALIDATION_LABELS'][i]] = list()
class_labels[globals()['VALIDATION_LABELS'][i]].append(globals()['VALIDATION_LABELS'][i])
class_entities[globals()['VALIDATION_LABELS'][i]].append(globals()['VALIDATION_DATA'][i])
for key in class_labels.keys():
label = np.asarray(class_labels[key] + ['9'])
entities = np.asarray(class_entities[key])
y_validate = to_categorical(label)[:-1]
print(y_validate.shape)
loss, acc, recall, precision = model.evaluate(x=entities, y=y_validate, batch_size=32)
results[key] = [loss, acc, recall, precision]
return results
def writeCSV2(variator: Variator, model: Model, counter=[0, 0]):
history = variator.histories[-1]
score = model.evaluate(tX, tY, verbose=0)
acc = history.history['acc'][-1]
val_acc = history.history['val_acc'][-1]
print("counter: " + str(counter[1]))
if score[1] > counter[1]:
counter[1] = score[1]
model_json = model.to_json()
with open("Models/JSON/modelEvCluster_Architecure.json",
"w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save("Models/Weights/bestEvModelCluster.hd5")
with open("Logs/modelStats.csv", "a") as f:
if counter[0] == 0:
counter[0] += 1
f.write("Model,acc,val_acc,test_acc\n")
f.write(variator.currentParameters['modelName'] + "," + str(acc) +
"," + str(val_acc) + "," + str(score[1]))
f.write("\n")
class BaseKerasModel(BaseModel):
model = None
tensorboard = None
train_names = ['train_loss', 'train_mse', 'train_mae']
val_names = ['val_loss', 'val_mse', 'val_mae']
counter = 0
inputs = None
hidden_layer = None
outputs = None
def __init__(self,
use_default_dense=True,
activation='relu',
kernel_regularizer=tf.keras.regularizers.l1(0.001)):
super().__init__()
if use_default_dense:
self.activation = activation
self.kernel_regularizer = kernel_regularizer
def create_input_layer(self, input_placeholder: BaseInputFormatter):
"""Creates keras model"""
self.inputs = tf.keras.layers.InputLayer(
input_shape=input_placeholder.get_input_state_dimension())
return self.inputs
def create_hidden_layers(self, input_layer=None):
if input_layer is None:
input_layer = self.inputs
hidden_layer = tf.keras.layers.Dropout(0.3)(input_layer)
hidden_layer = tf.keras.layers.Dense(
128,
kernel_regularizer=self.kernel_regularizer,
activation=self.activation)(hidden_layer)
hidden_layer = tf.keras.layers.Dropout(0.4)(hidden_layer)
hidden_layer = tf.keras.layers.Dense(
64,
kernel_regularizer=self.kernel_regularizer,
activation=self.activation)(hidden_layer)
hidden_layer = tf.keras.layers.Dropout(0.3)(hidden_layer)
hidden_layer = tf.keras.layers.Dense(
32,
kernel_regularizer=self.kernel_regularizer,
activation=self.activation)(hidden_layer)
hidden_layer = tf.keras.layers.Dropout(0.1)(hidden_layer)
self.hidden_layer = hidden_layer
return self.hidden_layer
def create_output_layer(self,
output_formatter: BaseOutputFormatter,
hidden_layer=None):
# sigmoid/tanh all you want on self.model
if hidden_layer is None:
hidden_layer = self.hidden_layer
self.outputs = tf.keras.layers.Dense(
output_formatter.get_model_output_dimension()[0],
activation='tanh')(hidden_layer)
self.model = Model(inputs=self.inputs, outputs=self.outputs)
return self.outputs
def write_log(self, callback, names, logs, batch_no, eval=False):
for name, value in zip(names, logs):
summary = tf.Summary()
summary_value = summary.value.add()
summary_value.simple_value = value
tag_name = name
if eval:
tag_name = 'eval_' + tag_name
summary_value.tag = tag_name
callback.writer.add_summary(summary, batch_no)
callback.writer.flush()
def finalize_model(self, logname=str(int(random() * 1000))):
loss, loss_weights = self.create_loss()
self.model.compile(tf.keras.optimizers.Nadam(lr=0.001),
loss=loss,
loss_weights=loss_weights,
metrics=[
tf.keras.metrics.mean_absolute_error,
tf.keras.metrics.binary_accuracy
])
log_name = './logs/' + logname
self.logger.info("log_name: " + log_name)
self.tensorboard = tf.keras.callbacks.TensorBoard(
log_dir=log_name,
histogram_freq=1,
write_images=False,
batch_size=1000,
)
self.tensorboard.set_model(self.model)
self.logger.info("Model has been finalized")
def fit(self, x, y, batch_size=1):
if self.counter % 200 == 0:
logs = self.model.evaluate(x, y, batch_size=batch_size, verbose=1)
self.write_log(self.tensorboard,
self.model.metrics_names,
logs,
self.counter,
eval=True)
print('step:', self.counter)
else:
logs = self.model.train_on_batch(x, y)
self.write_log(self.tensorboard, self.model.metrics_names, logs,
self.counter)
self.counter += 1
def predict(self, arr):
return self.model.predict(arr)
def save(self, file_path):
self.model.save_weights(filepath=file_path, overwrite=True)
def load(self, file_path):
path = os.path.abspath(file_path)
self.model.load_weights(filepath=os.path.abspath(file_path))
def create_loss(self):
return 'mean_absolute_error', None
def main(arg):
directory = Path('./saved_predictions/')
directory.mkdir(exist_ok=True)
directory = Path('./saved_models/')
directory.mkdir(exist_ok=True)
directory = Path('./training_checkpoints/')
directory.mkdir(exist_ok=True)
input_yx_size = tuple(args.input_yx_size)
batch_size = args.batch_size
epochs = args.epochs
learning_rate = args.learning_rate
num_test_samples = args.num_test_samples
save_weights = args.save_weights
every = args.every
num_samples = args.num_samples
save_train_prediction = args.save_train_prediction
save_test_prediction = args.save_test_prediction
verbose = args.verbose
validation_ratio = args.validation_ratio
y_axis_len, x_axis_len = input_yx_size
decay = args.decay
decay = args.decay
load_weights = args.load_weights
y_axis_len, x_axis_len = input_yx_size
num_points = y_axis_len * x_axis_len
is_flat_channel_in = args.is_flat_channel_in
input_points = Input(shape=(num_points, 4))
x = input_points
x = Convolution1D(64, 1, activation='relu', input_shape=(num_points, 4))(x)
x = BatchNormalization()(x)
x = Convolution1D(128, 1, activation='relu')(x)
x = BatchNormalization()(x)
x = Convolution1D(512, 1, activation='relu')(x)
x = BatchNormalization()(x)
x = MaxPooling1D(pool_size=num_points)(x)
x = Dense(512, activation='relu')(x)
x = BatchNormalization()(x)
x = Dense(256, activation='relu')(x)
x = BatchNormalization()(x)
x = Dense(16,
weights=[
np.zeros([256, 16]),
np.array([1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
1]).astype(np.float32)
])(x)
input_T = Reshape((4, 4))(x)
# forward net
g = Lambda(mat_mul, arguments={'B': input_T})(input_points)
g = Convolution1D(64, 1, input_shape=(num_points, 3), activation='relu')(g)
g = BatchNormalization()(g)
g = Convolution1D(64, 1, input_shape=(num_points, 3), activation='relu')(g)
g = BatchNormalization()(g)
# feature transformation net
f = Convolution1D(64, 1, activation='relu')(g)
f = BatchNormalization()(f)
f = Convolution1D(128, 1, activation='relu')(f)
f = BatchNormalization()(f)
f = Convolution1D(128, 1, activation='relu')(f)
f = BatchNormalization()(f)
f = MaxPooling1D(pool_size=num_points)(f)
f = Dense(512, activation='relu')(f)
f = BatchNormalization()(f)
f = Dense(256, activation='relu')(f)
f = BatchNormalization()(f)
f = Dense(64 * 64,
weights=[
np.zeros([256, 64 * 64]),
np.eye(64).flatten().astype(np.float32)
])(f)
feature_T = Reshape((64, 64))(f)
# forward net
g = Lambda(mat_mul, arguments={'B': feature_T})(g)
seg_part1 = g
g = Convolution1D(64, 1, activation='relu')(g)
g = BatchNormalization()(g)
g = Convolution1D(32, 1, activation='relu')(g)
g = BatchNormalization()(g)
g = Convolution1D(32, 1, activation='relu')(g)
g = BatchNormalization()(g)
# global_feature
global_feature = MaxPooling1D(pool_size=num_points)(g)
global_feature = Lambda(exp_dim, arguments={'num_points':
num_points})(global_feature)
# point_net_seg
c = concatenate([seg_part1, global_feature])
""" c = Convolution1D(512, 1, activation='relu')(c)
c = BatchNormalization()(c)
c = Convolution1D(256, 1, activation='relu')(c)
c = BatchNormalization()(c)
c = Convolution1D(128, 1, activation='relu')(c)
c = BatchNormalization()(c)
c = Convolution1D(128, 1, activation='relu')(c)
c = BatchNormalization()(c) """
c = Convolution1D(256, 1, activation='relu')(c)
c = BatchNormalization()(c)
c = Convolution1D(128, 4, activation='relu', strides=4)(c)
c = BatchNormalization()(c)
c = Convolution1D(128, 4, activation='relu', strides=4)(c)
c = BatchNormalization()(c)
c = Convolution1D(128, 4, activation='relu', strides=4)(c)
c = BatchNormalization()(c)
c = Convolution1D(64, 4, activation='relu', strides=4)(c)
c = BatchNormalization()(c)
c = Convolution1D(64, 4, activation='relu', strides=4)(c)
c = BatchNormalization()(c)
c = Convolution1D(32, 1, activation='relu')(c)
c = BatchNormalization()(c)
""" c = Convolution1D(128, 4, activation='relu',strides=4)(c)
c = Convolution1D(64, 4, activation='relu',strides=4)(c)
c = Convolution1D(32, 4, activation='relu',strides=4)(c)
c = Convolution1D(16, 1, activation='relu')(c)
c = Convolution1D(1, 1, activation='relu')(c) """
#c = tf.keras.backend.squeeze(c,3);
c = CuDNNLSTM(64, return_sequences=False)(c)
#c =CuDNNLSTM(784, return_sequences=False))
#c =CuDNNLSTM(256, return_sequences=False))
#c = Reshape([16,16,1])(c)
c = Reshape([8, 8, 1])(c)
c = Conv2DTranspose(8, (3, 3),
padding="same",
activation="relu",
strides=(2, 2))(c)
c = Conv2DTranspose(8, (3, 3), padding="valid", activation="relu")(c)
#c =Dropout(0.4))
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(16, (3, 3), padding="valid", activation="relu")(c)
#c =Dropout(0.4))
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(32, (3, 3), padding="valid", activation="relu")(c)
#c =Dropout(0.4))
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(32, (3, 3), padding="valid", activation="relu")(c)
#c =Dropout(0.4))
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(32, (3, 3), padding="valid", activation="relu")(c)
#c =Dropout(0.4))
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(64, (3, 3), padding="valid", activation="relu")(c)
#c =Dropout(0.4))
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(64, (3, 3), padding="valid", activation="relu")(c)
#c =Dropout(0.4))
c = tf.keras.layers.BatchNormalization()(c)
#c =Dropout(0.4))
c = Conv2DTranspose(128, (3, 3),
padding="same",
activation="relu",
strides=(2, 2))(c)
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(128, (3, 3), padding="valid", activation="relu")(c)
#c =Dropout(0.4))
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(128, (3, 3),
padding="same",
activation="relu",
strides=(2, 2))(c)
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(128, (3, 3), padding="valid", activation="relu")(c)
c = tf.keras.layers.BatchNormalization()(c)
#c =Dropout(0.4))
#c =tf.keras.layers.BatchNormalization())
c = Conv2DTranspose(64, (3, 3), padding="same", strides=(4, 2))(c)
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(32, (3, 3), padding="valid", activation="relu")(c)
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(32, (3, 3), padding="valid", activation="relu")(c)
c = tf.keras.layers.BatchNormalization()(c)
#c =Dropout(0.4))
c = Conv2DTranspose(32, (3, 3),
padding="same",
activation="relu",
strides=(1, 1))(c)
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(32, (3, 1), padding="valid", activation="relu")(c)
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(32, (3, 1), padding="valid", activation="relu")(c)
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(16, (1, 1), padding="valid", activation="relu")(c)
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(8, (1, 1), padding="valid", activation="relu")(c)
c = tf.keras.layers.BatchNormalization()(c)
c = Conv2DTranspose(1, (1, 1), padding="valid")(c)
""" c =Conv2DTranspose(4, (1,1),padding="same",activation="relu"))
c =Conv2DTranspose(2, (1,1),padding="same",activation="relu"))
#c =Dropout(0.4))
c =Conv2DTranspose(1, (1,1),padding="same")) """
prediction = tf.keras.layers.Reshape([512, 256])(c)
""" c1 ,c2 = tf.split(c,[256,256],axis=1,name="split")
complexNum = tf.dtypes.complex(
c1,
c2,
name=None
)
complexNum =tf.signal.ifft2d(
complexNum,
name="IFFT"
)
real = tf.math.real(complexNum)
imag = tf.math.imag(complexNum)
con = concatenate([real,imag])
prediction =tf.keras.layers.Reshape([ 512, 256])(con)
"""
# define model
model = Model(inputs=input_points, outputs=prediction)
opt = tf.keras.optimizers.Adam(lr=learning_rate, decay=decay)
loss = tf.keras.losses.MeanSquaredError()
mertric = ['mse']
if args.loss is "MAE":
loss = tf.keras.losses.MeanAbsoluteError()
mertric = ['mae']
model.compile(
loss=loss,
optimizer=opt,
metrics=mertric,
)
model.summary()
if load_weights:
model.load_weights('./training_checkpoints/cp-best_loss.ckpt')
#edit data_loader.py if you want to play with data
input_ks, ground_truth = load_data(num_samples,
is_flat_channel_in=is_flat_channel_in)
input_ks = input_ks / np.max(input_ks)
checkpoint_path = "./training_checkpoints/cp-{epoch:04d}.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
# Create checkpoint callback
#do you want to save the model's wieghts? if so set this varaible to true
cp_callback = []
NAME = "NUFFT_NET"
tensorboard = TensorBoard(log_dir="logs/{}".format(NAME))
cp_callback.append(tensorboard)
if save_weights:
cp_callback.append(
tf.keras.callbacks.ModelCheckpoint(checkpoint_dir,
save_weights_only=True,
verbose=verbose,
period=every))
if args.is_train:
model.fit(input_ks,
ground_truth,
batch_size=batch_size,
epochs=epochs,
validation_split=validation_ratio,
callbacks=cp_callback)
if args.name_model is not "":
model.save('./saved_mdoels/' + args.name_model)
dict_name = './saved_predictions/'
#return to image size
x_axis_len = int(x_axis_len / 4)
np.random.seed(int(time()))
if save_train_prediction <= num_samples:
rand_ix = np.random.randint(0, num_samples - 1, save_train_prediction)
#kspace = np.zeros((save_train_prediction,
#y_axis_len,input_ks[rand_ix].shape[1]))
kspace = input_ks[rand_ix]
if args.save_input:
np.save("./saved_predictions/inputs.npy", input_ks[rand_ix])
ground_truth = ground_truth[rand_ix]
preds = model.predict(kspace, batch_size=save_train_prediction)
for i in range(save_train_prediction):
output = np.reshape(preds[i], (y_axis_len * 2, x_axis_len))
output = output * 255
output[np.newaxis, ...]
output_gt = ground_truth[i]
output_gt[np.newaxis, ...]
output = np.concatenate([output, output_gt], axis=0)
np.save(dict_name + 'prediction%d.npy' % (i + 1), output)
input_ks, ground_truth = load_data(
num_test_samples, 'test', is_flat_channel_in=is_flat_channel_in)
input_ks = input_ks / np.max(input_ks)
if args.is_eval:
model.evaluate(input_ks,
ground_truth,
batch_size,
verbose,
callbacks=cp_callback)
if save_test_prediction <= num_test_samples:
rand_ix = np.random.randint(0, num_test_samples - 1,
save_test_prediction)
kspace = input_ks[rand_ix]
if args.save_input:
np.save("./saved_predictions/test_inputs.npy", input_ks[rand_ix])
ground_truth = ground_truth[rand_ix]
preds = model.predict(kspace, batch_size=save_test_prediction)
for i in range(save_test_prediction):
output = np.reshape(preds[i], (y_axis_len * 2, x_axis_len))
output = output * 255
output[np.newaxis, ...]
output_gt = ground_truth[i]
output_gt[np.newaxis, ...]
output = np.concatenate([output, output_gt], axis=0)
np.save(dict_name + 'test_prediction%d.npy' % (i + 1), output)
y = tf.keras.layers.Dense(units=32,
activation='elu',
kernel_initializer='he_uniform')(y)
y = tf.keras.layers.Dense(units=2,
activation='softmax',
kernel_initializer='he_uniform')(y)
wenz_model = Model(inputs=[input1, input2], outputs=y)
adam = Adam(lr=0.02, decay=0.01)
wenz_model.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=['accuracy'])
checkpoint_path = "training/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
# Create a callback that saves the model's weights
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=True,
verbose=1)
wenz_model.fit(XY, epochs=10,
callbacks=[cp_callback]) # add validation training_data?
test_loss, test_acc = wenz_model.evaluate(XYt, verbose=2)
print('\nTest accuracy:', test_acc)
wenz_model.save(
'/home/pirate/PycharmProjects/SchafkopfAI/models/trained_models/test-wenz-prediction6'
)
store.close()
save_best_only=True)
lr_scheduler = LearningRateScheduler(lr_schedule)
lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
cooldown=0,
patience=5,
min_lr=0.5e-6)
callbacks = [checkpoint, lr_reducer, lr_scheduler]
# choose training configs
sgd = SGD(lr=0.001, momentum=0.9)
model.compile(optimizer=sgd,
loss=categorical_crossentropy,
metrics=['accuracy'])
model.summary()
# train
hist = model.fit(x_train,
y_train,
batch_size=100,
epochs=10,
shuffle=True,
verbose=1,
validation_split=0.1,
callbacks=callbacks)
# test
model.evaluate(x_test, y_test, verbose=1)
def train_test_model2(hparams):
start_time = time.time()
input = Input(shape=(X_train.shape[1:]))
output = tf.expand_dims(input, axis=-1)
output = GRU(units=hparams[HP_NUM_UNITS],
activation="relu",
return_sequences=True)(output)
output = GRU(
units=hparams[HP_NUM_UNITS],
activation="relu",
)(output)
#dropout_out = Dropout(0.8)(output)
# Adding some further layers (replace or remove with your architecture):
out = Dense(units=20, activation="relu")(output)
# Building model:
model = Model(inputs=input, outputs=out)
model.compile(loss='mean_squared_error',
optimizer="rmsprop",
metrics=['acc'])
print(model.summary())
plot_model(model,
to_file='model_plot' + dateAndTimeNow + '.png',
show_shapes=True,
show_layer_names=True)
model.fit(X_train,
y_train,
epochs=epochs,
batch_size=hparams[HP_BATCH_SIZE],
verbose=1,
callbacks=[
tf.keras.callbacks.TensorBoard("logs/fit/" + dateAndTimeNow)
]) # Run with 1 epoch to speed things up for demo purposes
test_predictions = model.predict(X_test)
y_test2 = np.argmax(y_test, axis=1)
test_predictions = np.round(test_predictions)
sums = np.sum(test_predictions, axis=1)
test_predictions2 = np.argmax(test_predictions, axis=1)
#print(sklearn.metrics.multilabel_confusion_matrix(y_test2, test_predictions2))
_, accuracy = model.evaluate(X_test, y_test)
evaluate_model("GRU" + str(hparams[HP_NUM_UNITS]), y_test2,
test_predictions2)
print("Total Time" + str(time.time() - start_time))
"""
# Best 0.96
#step = X_train.shape[1]
model = Sequential()
#model.add(Embedding(10000,300,input_length=1))
model.add(GRU(input_shape=(100), units=10, activation="softmax"))
model.add(Dense(20, activation="softmax"))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['acc'])
model.summary()
plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
model.fit(X_train, y_train, epochs=epochs, batch_size=hparams[HP_BATCH_SIZE]) # Run with 1 epoch to speed things up for demo purposes
test_predictions = model.predict(X_test)
"""
return accuracy
x = Conv1D(32, 4, padding="same", activation='relu')(inputs)
x = Conv1D(64, 4, padding="same", activation='relu')(x)
x = Conv1D(128, 2, padding="same", activation='relu')(x)
x = MaxPooling1D(pool_size=(3))(x)
# print(x.shape)
# x = Flatten()(x)
# x = Reshape((-1,-1))(x)
x = LSTM(64, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)(x)
x = Dense(128, activation='relu')(x)
x = Dense(emotion_len, activation='softmax')(x)
model = Model(inputs=inputs, outputs=x)
#model.summary()
model.compile(loss=categorical_crossentropy,
optimizer=RMSprop(),
metrics=['accuracy'])
history = model.fit(X_train,
Y_train,
batch_size=32,
epochs=50,
validation_data=(X_test, Y_test),
verbose=1,
shuffle=True)
loss, acc = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', loss)
print('Test accuracy:', acc)
