How can i set min and max values so the model only predicts Y output between them?
model = Sequential([
Dense(units=100, input_shape=(3, ), activation='tanh'),
Dense(units=18, activation='tanh'),
Dense(units=1, activation='tanh'),
Dense(units=1, activation='softmax')
])
opt = keras.optimizers.SGD(learning_rate=0.0001, momentum=0.9999, nesterov=False)
model.compile(optimizer=opt, loss='mae', metrics=[tf.keras.metrics.MeanAbsoluteError()])
model.fit(df, target, epochs=300, shuffle=False, verbose=1, callbacks=[estop, rlronp], validation_split=0.2)
tf.clip_by_value:
tf.clip_by_value(
t, clip_value_min, clip_value_max, name=None
)
In your code:
Dense(units=1, activation=lambda x: tf.clip_by_value(x, -5, 5))
Related
def custom_metric(y_prem):
def score_func(y_true, y_pred):
diff = y_pred - y_true
return tf.reduce_sum(diff[y_prem>=y_pred])
return score_func
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(32, input_shape=[len(X_train[0, :])], activation='tanh'),
tf.keras.layers.Dense(8, input_shape=[len(X_train[0, :])], activation='linear'),
tf.keras.layers.Dense(4, input_shape=[len(X_train[0, :])], activation='tanh'),
tf.keras.layers.Dense(1, activation='relu'),
])
model.compile(optimizer='adam', loss='mean_squared_error', metrics=[custom_metric(y_prem)])
model.summary()
model.fit(X_train_minmax, y_train, epochs=30, batch_size=len(y_train))
y_prem and y_train are both the same size(50646)
I have tried to define this custom metric function where y_prem is a vector in the size of the prediction. I want to sum the diff between the pred and the true only on the indexes where the pred is lower than y_prem but when I trained the model I received an error message:
File "C:/Users/zehavi kelman/PycharmProjects/Accident_predicting/simpego_test.py", line 61, in score_func *
return K.sum(diff[y_prem>=y_pred])
ValueError: Shapes (50646, 1) and (50646, 50646) are incompatible
How can I fix that?
I am not sure of what you want to do but I implemented a reproducible example that do not output an error message (pay attention to the x and y shapes):
import tensorflow as tf
x = tf.random.uniform(shape=[50646, 5], minval=0, maxval=1)
y = tf.random.uniform(shape=[50646, 1], minval=0, maxval=1)
y_prem = tf.random.uniform(shape=[50646, 1], minval=0, maxval=1)
def custom_metric(y_prem):
def score_func(y_true, y_pred):
diff = y_pred - y_true
return tf.reduce_sum(diff[y_prem>=y_pred])
return score_func
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(32, input_shape=[len(x[0, :])], activation='tanh'),
tf.keras.layers.Dense(8, activation='linear'),
tf.keras.layers.Dense(4, activation='tanh'),
tf.keras.layers.Dense(1, activation='relu'),
])
model.compile(optimizer='adam', loss='mean_squared_error', metrics=[custom_metric(y_prem)])
model.summary()
model.fit(x, y, epochs=30, batch_size=len(y))
I am implementing a fully-connected model for classification using the MNIST dataset. A part of the code is the following:
model=tf.keras.models.Sequential([
tf.keras.layers.Input(shape=(28, 28, 1)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(
loss='categorical_crossentropy',
optimizer=tf.optimizers.SGD(),
metrics=["accuracy"]
)
model.fit(
x_train,
y_train,
batch_size=64,
epochs=3,
validation_data=(x_test, y_test)
)
Is there a way to print the max gradient for each layer for a given mini-batch?
Define a custom training loop instead of invoking compile() and fit().
optimizer=tf.keras.optimizers.Adam(0.001)
loss=tf.keras.losses.SparseCategoricalCrossentropy()
for x, y in zip(x_train, y_train):
with tf.GradientTape() as tape:
predictions = model(x)
loss_value = loss(y, predictions)
gradients = tape.gradient(loss_value, model.trainable_weights)
grads_and_vars = zip(gradients, model.trainable_weights)
optimizer.apply_gradients(grads_and_vars)
for layer in range(0, 4): # for 4 layers
print('max gradient of layer={}, kernel={}, bias={}'.format(
layer, gradients[layer].numpy().max(), gradients[layer*2+1].numpy().max()))
Check this out : About Keras
I have created a data set of y ~ x**2
However, when I train a neural network, it just can't fit a quadratic equation.
This is my model.
model2 = tf.keras.models.Sequential(
[tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(1)]
)
loss = tf.keras.losses.mse
optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)
model2.compile(optimizer=optimizer, loss=loss, metrics=tf.metrics.RootMeanSquaredError())
model2.fit(tf.expand_dims(X_train, -1), y_train, epochs=1000, verbose=1)
My thought process of above model is that I thought that each relu activation will fit a local linear line, and slowly connect all the neurons to form a quadratic line.
In the end, I managed fit it by using an activation of lambda x:x**2 on the output layer, However, that is because I know the function is an x**2.
So my question is, without knowing the true function, how do I train a neural network to fit a non-linear curve?
Your code works fine for me.
Note, I use larger learning rate and an early stop (with 300 patience of total 2000 epochs).
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
train_x = np.linspace(0, 80, 160)
train_y = train_x**2
test_x = np.linspace(80, 100, 40)
test_y = test_x**2
model2 = tf.keras.models.Sequential(
[tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(100, activation='relu'),
tf.keras.layers.Dense(1)]
)
loss = tf.keras.losses.mse
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2)
early_stop = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=300, restore_best_weights=True)
model2.compile(optimizer=optimizer, loss=loss, metrics=tf.metrics.RootMeanSquaredError())
model2.fit(tf.expand_dims(train_x, -1), train_y, epochs=2000, verbose=1, callbacks=[early_stop])
train_pred = model2.predict(train_x)
test_pred = model2.predict(test_x)
plt.scatter(train_x, train_y, c='blue', label='train x')
plt.scatter(test_x, test_y, c='green', label='test x')
plt.scatter(train_x, train_pred, c='red', label='train pred')
plt.scatter(test_x, test_pred, c='orange', label='test pred')
plt.legend()
plt.show()
Training and test results photo here
I am trying to create a Multitask NN using Tensorflow. Following is the architecture that I am trying to develop:
METRICS= [tf.keras.metrics.TruePositives(name='TP'),
tf.keras.metrics.FalsePositives(name='FP'),
tf.keras.metrics.TrueNegatives(name='TN'),
tf.keras.metrics.FalseNegatives(name='FN'),
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
tf.keras.metrics.AUC(curve='PR', name='PR-AUC')]
input_shape = (X_train.shape[1],)
inputlayer = tf.keras.layers.Input(shape=input_shape)
l1 = tf.keras.layers.Dense(input_shape[0]*2, activation= 'relu')(inputlayer)
l2 = tf.keras.layers.Dropout(0.1)(l1)
l3 = tf.keras.layers.Dense(int(input_shape[0]/2), activation='relu')(l2)
output1 = tf.keras.layers.Dense(1, activation='sigmoid', name = 'output1')(l3)
output2 = tf.keras.layers.Dense(10, activation='softmax', name = 'output2')(l3)
output3 = tf.keras.layers.Dense(12, activation='softmax', name = 'output3')(l3)
model = tf.keras.Model(inputs=inputlayer, outputs=[output1, output2, output3])
model.compile(loss={"output1": 'binary_crossentropy',
"output2": 'categorical_crossentropy',
"output3": 'categorical_crossentropy'},
optimizer=tf.keras.optimizers.Adam(learning_rate=.01),
metrics = METRICS, loss_weights = [1, 1e-1, 1e-1])
And this is the model architecture:
Then I tried to train the model like this:
BATCH_SIZE= 20
model.fit(X_train, [y1_train,y2_train,y3_train], batch_size=BATCH_SIZE, epochs=10, verbose=0)
But I got the following issue:
ValueError: Shapes (None, 1) and (None, 10) are incompatible
I already verified the labels of each output and they are respectively 2, 10 and 12
I couldn't understood what the problem is exactly, can anyone give me a suggestion please?
I think you might have mixed up the order of your labels. Here is a working example:
import tensorflow as tf
METRICS= [tf.keras.metrics.TruePositives(name='TP'),
tf.keras.metrics.FalsePositives(name='FP'),
tf.keras.metrics.TrueNegatives(name='TN'),
tf.keras.metrics.FalseNegatives(name='FN'),
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
tf.keras.metrics.AUC(curve='PR', name='PR-AUC')]
input_shape = (31,)
inputlayer = tf.keras.layers.Input(shape=input_shape)
l1 = tf.keras.layers.Dense(input_shape[0]*2, activation= 'relu')(inputlayer)
l2 = tf.keras.layers.Dropout(0.1)(l1)
l3 = tf.keras.layers.Dense(int(input_shape[0]/2), activation='relu')(l2)
output1 = tf.keras.layers.Dense(1, activation='sigmoid', name = 'output1')(l3)
output2 = tf.keras.layers.Dense(10, activation='softmax', name = 'output2')(l3)
output3 = tf.keras.layers.Dense(12, activation='softmax', name = 'output3')(l3)
model = tf.keras.Model(inputs=inputlayer, outputs=[output1, output2, output3])
model.compile(loss={"output1": 'binary_crossentropy',
"output2": 'categorical_crossentropy',
"output3": 'categorical_crossentropy'},
optimizer=tf.keras.optimizers.Adam(learning_rate=.01),
metrics = METRICS, loss_weights = [1, 1e-1, 1e-1])
y1_train, y2_train, y3_train = tf.random.uniform((50, 1), maxval=2), tf.random.uniform((50, 10), maxval=11), tf.random.uniform((50, 12), maxval=13)
model.fit(tf.random.normal((50, 31)), [y1_train,y2_train,y3_train], batch_size=20, epochs=10)
You need to make sure that y1_train, y2_train, and y3_train are in the correct order and have the correct shape, that is (samples, 1), (samples, 10), and (samples, 12).
i want to see precision and recall for my model for a binary image classification but i can find how do to that
Here is my code
x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(1024, activation='relu')(x)
x = tf.keras.layers.Dense(1024, activation='relu')(x)
x = tf.keras.layers.Dense(512, activation='relu')(x)
preds = tf.keras.layers.Dense(2, activation='softmax')(x)
model = tf.keras.Model(inputs = base_model.input, outputs = preds)
for layer in model.layers[:175]:
layer.trainable = False
for layer in model.layers[175:]:
layer.trainable = True
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit_generator(generator=train_generator,
epochs=20,
steps_per_epoch=step_size_train,
validation_data = test_generator,
validation_steps=step_size_test)```
If you want precision and recall during train then you can add precision and recall metrics to the metrics list during model compilation as below
model.compile(optimizer='Adam', loss='categorical_crossentropy',
metrics=['accuracy',
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall()])
Example
input = tf.keras.layers.Input(8)
x = tf.keras.layers.Dense(4, activation='relu')(input)
output = tf.keras.layers.Dense(2, activation='softmax')(x)
model = tf.keras.Model(inputs = input, outputs = output)
model.compile(optimizer='Adam', loss='categorical_crossentropy',
metrics=['accuracy',
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall()])
X = np.random.randn(100,8)
y = np.random.randint(0,2, (100, 2))
model.fit(X, y, epochs=10)