I'm trying to make the most basic of basic neural networks to get familiar with functional API in Tensorflow 2.x.
Basically what I'm trying to do is the following with my simplified iris dataset (i.e. setosa or not)
Use the 4 features as input
Dense layer of 3
Sigmoid activation function
Dense layer of 2 (one for each class)
Softmax activation
Binary cross entropy / log-loss as my loss function
However, I can't figure out how to control one key aspect of the model. That is, how can I ensure that each feature from my input layer contributes to only one neuron in my subsequent dense layer? Also, how can I allow a feature to contribute to more than one neuron?
This isn't clear to me from the documentation.
# Load data
from sklearn.datasets import load_iris
import pandas as pd
iris = load_iris()
X, y = load_iris(return_X_y=True, as_frame=True)
X = X.astype("float32")
X.index = X.index.map(lambda i: "iris_{}".format(i))
X.columns = X.columns.map(lambda j: j.split(" (")[0].replace(" ","_"))
y.index = X.index
y = y.map(lambda i:iris.target_names[i])
y_simplified = y.map(lambda i: {True:1, False:0}[i == "setosa"])
y_simplified = pd.get_dummies(y_simplified, columns=["setosa", "not_setosa"])
# Traing test split
from sklearn.model_selection import train_test_split
seed=0
X_train,X_test, y_train,y_test= train_test_split(X,y_simplified, test_size=0.3, random_state=seed)
# Simple neural network
import tensorflow as tf
tf.random.set_seed(seed)
# Input[4 features] -> Dense layer of 3 neurons -> Activation function -> Dense layer of 2 (one per class) -> Softmax
inputs = tf.keras.Input(shape=(4))
x = tf.keras.layers.Dense(3)(inputs)
x = tf.keras.layers.Activation(tf.nn.sigmoid)(x)
x = tf.keras.layers.Dense(2)(x)
outputs = tf.keras.layers.Activation(tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs, name="simple_binary_iris")
model.compile(loss="binary_crossentropy", metrics=["accuracy"] )
model.summary()
history = model.fit(X_train, y_train, batch_size=64, epochs=10, validation_split=0.2)
test_scores = model.evaluate(X_test, y_test)
print("Test loss:", test_scores[0])
print("Test accuracy:", test_scores[1])
Results:
Model: "simple_binary_iris"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_44 (InputLayer) [(None, 4)] 0
_________________________________________________________________
dense_96 (Dense) (None, 3) 15
_________________________________________________________________
activation_70 (Activation) (None, 3) 0
_________________________________________________________________
dense_97 (Dense) (None, 2) 8
_________________________________________________________________
activation_71 (Activation) (None, 2) 0
=================================================================
Total params: 23
Trainable params: 23
Non-trainable params: 0
_________________________________________________________________
Epoch 1/10
2/2 [==============================] - 0s 40ms/step - loss: 0.6344 - accuracy: 0.6667 - val_loss: 0.6107 - val_accuracy: 0.7143
Epoch 2/10
2/2 [==============================] - 0s 6ms/step - loss: 0.6302 - accuracy: 0.6667 - val_loss: 0.6083 - val_accuracy: 0.7143
Epoch 3/10
2/2 [==============================] - 0s 7ms/step - loss: 0.6278 - accuracy: 0.6667 - val_loss: 0.6056 - val_accuracy: 0.7143
Epoch 4/10
2/2 [==============================] - 0s 7ms/step - loss: 0.6257 - accuracy: 0.6667 - val_loss: 0.6038 - val_accuracy: 0.7143
Epoch 5/10
2/2 [==============================] - 0s 7ms/step - loss: 0.6239 - accuracy: 0.6667 - val_loss: 0.6014 - val_accuracy: 0.7143
Epoch 6/10
2/2 [==============================] - 0s 7ms/step - loss: 0.6223 - accuracy: 0.6667 - val_loss: 0.6002 - val_accuracy: 0.7143
Epoch 7/10
2/2 [==============================] - 0s 7ms/step - loss: 0.6209 - accuracy: 0.6667 - val_loss: 0.5989 - val_accuracy: 0.7143
Epoch 8/10
2/2 [==============================] - 0s 7ms/step - loss: 0.6195 - accuracy: 0.6667 - val_loss: 0.5967 - val_accuracy: 0.7143
Epoch 9/10
2/2 [==============================] - 0s 7ms/step - loss: 0.6179 - accuracy: 0.6667 - val_loss: 0.5953 - val_accuracy: 0.7143
Epoch 10/10
2/2 [==============================] - 0s 7ms/step - loss: 0.6166 - accuracy: 0.6667 - val_loss: 0.5935 - val_accuracy: 0.7143
2/2 [==============================] - 0s 607us/step - loss: 0.6261 - accuracy: 0.6444
Test loss: 0.6261375546455383
Test accuracy: 0.644444465637207
how can I ensure that each feature from my input layer contributes to
only one neuron in my subsequent dense layer?
Have one input layer per feature and feed each input layer to a separate dense layer. Later you can concatenate the output of all the dense layers and proceed.
NOTE: One neuron can take any size input (in this case the input size is 1 as you want one feature to be used by the neuron) and the output size if always 1. A Dense layer with with n units will have n neurons and and so will have output size of n.
Working Sample
import tensorflow as tf
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
# Model architecutre
x1 = tf.keras.Input(shape=(1,))
x2 = tf.keras.Input(shape=(1,))
x3 = tf.keras.Input(shape=(1,))
x4 = tf.keras.Input(shape=(1,))
x1_ = tf.keras.layers.Dense(3, activation=tf.nn.relu)(x1)
x2_ = tf.keras.layers.Dense(3, activation=tf.nn.relu)(x2)
x3_ = tf.keras.layers.Dense(3, activation=tf.nn.relu)(x3)
x4_ = tf.keras.layers.Dense(3, activation=tf.nn.relu)(x4)
merged = tf.keras.layers.concatenate([x1_, x2_, x3_, x4_])
merged = tf.keras.layers.Dense(16, activation=tf.nn.relu)(merged)
outputs = tf.keras.layers.Dense(3, activation=tf.nn.softmax)(merged)
model = tf.keras.Model(inputs=[x1,x2,x3,x4], outputs=outputs)
model.compile(loss="sparse_categorical_crossentropy", metrics=["accuracy"] )
# Load and prepare data
iris = load_iris()
X = iris.data
y = iris.target
X_train,X_test, y_train,y_test= train_test_split(X,y, test_size=0.3)
# Fit the model
model.fit([X_train[:,0],X_train[:,1],X_train[:,2],X_train[:,3]], y_train, batch_size=64, epochs=100, validation_split=0.25)
# Evaluate the model
test_scores = model.evaluate([X_test[:,0],X_test[:,1],X_test[:,2],X_test[:,3]], y_test)
print("Test loss:", test_scores[0])
print("Test accuracy:", test_scores[1])
Output:
Epoch 1/100
2/2 [==============================] - 0s 75ms/step - loss: 1.6446 - accuracy: 0.4359 - val_loss: 1.6809 - val_accuracy: 0.5185
Epoch 2/100
2/2 [==============================] - 0s 10ms/step - loss: 1.4151 - accuracy: 0.6154 - val_loss: 1.4886 - val_accuracy: 0.5556
Epoch 3/100
2/2 [==============================] - 0s 9ms/step - loss: 1.2725 - accuracy: 0.6795 - val_loss: 1.3813 - val_accuracy: 0.5556
Epoch 4/100
2/2 [==============================] - 0s 9ms/step - loss: 1.1829 - accuracy: 0.6795 - val_loss: 1.2779 - val_accuracy: 0.5926
Epoch 5/100
2/2 [==============================] - 0s 10ms/step - loss: 1.0994 - accuracy: 0.6795 - val_loss: 1.1846 - val_accuracy: 0.5926
Epoch 6/100
.................. [ Truncated ]
Epoch 100/100
2/2 [==============================] - 0s 2ms/step - loss: 0.4049 - accuracy: 0.9333
Test loss: 0.40491223335266113
Test accuracy: 0.9333333373069763
Pictorial representation of the above model architecture
Dense layers in Keras/TF are fully connected layers. For example, when you use a Dense layer as follows
inputs = tf.keras.Input(shape=(4))
x = tf.keras.layers.Dense(3)(inputs)
all the 4 connected input neurons are connected to all the 3 output neurons.
There isn't any predefined layer in Keras/TF to specify how to connect input and output neurons. However, Keras/TF is very flexible in that it allows you to define your custom layers easily.
Borrowing the idea from this answer, you could define a CustomConnected layer as follows:
class CustomConnected(tf.keras.layers.Dense):
def __init__(self, units, connections, **kwargs):
self.connections = connections
super(CustomConnected, self).__init__(units, **kwargs)
def call(self, inputs):
self.kernel = self.kernel * self.connections
return super(CustomConnected, self).call(inputs)
Using this layer, you can then specify the connections between two layers through the connections argument. For example:
inputs = tf.keras.Input(shape=(4))
connections = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1]])
x = CustomConnected(3, connections)(inputs)
Here, the 1st, 2nd, and 3rd input neurons are connected to the 1st, 2nd, and 3rd output neurons, respectively. Additionally, the 4th input neuron is connected to the 3rd output neuron.
UPDATE: As discussed in the comments section, an adaptive approach (e.g. by using only the maximum weight for each output neuron) is also possible but not recommended. You could implement this via the following layer:
class CustomSparse(tf.keras.layers.Dense):
def __init__(self, units, **kwargs):
super(CustomSparse, self).__init__(units, **kwargs)
def call(self, inputs):
nb_in, nb_out = self.kernel.shape
argmax = tf.argmax(self.kernel, axis=0) # Shape=(nb_out,)
argmax_onehot = tf.transpose(tf.one_hot(argmax, depth=nb_in)) # Shape=(nb_in, nb_out)
kernel_max = self.kernel * argmax_onehot
# tf.print(kernel_max) # Uncomment this line to print the weights
out = tf.matmul(inputs, kernel_max)
if self.bias is not None:
out += self.bias
if self.activation is not None:
out = self.activation(out)
return out
The main issue of this approach is that you cannot propagate gradients through the argmax operation required to select the maximum weight. As a result, the network will only "switch input neurons" when the selected weight is no longer the maximum weight.
Related
I'm trying to create a small transformer model with Keras to model stock prices, based off of this tutorial from the Keras docs. The problem is, my test loss is massive and barely changes between epochs, unsurprisingly resulting in severe underfitting, with my outputs all the same arbitrary value.
My code is below:
def transformer_encoder_block(inputs, head_size, num_heads, filters, dropout=0):
# Normalization and Attention
x = layers.LayerNormalization(epsilon=1e-6)(inputs)
x = layers.MultiHeadAttention(
key_dim=head_size, num_heads=num_heads, dropout=dropout
)(x, x)
x = layers.Dropout(dropout)(x)
res = x + inputs
# Feed Forward Part
x = layers.LayerNormalization(epsilon=1e-6)(res)
x = layers.Conv1D(filters=filters, kernel_size=1, activation="relu")(x)
x = layers.Dropout(dropout)(x)
x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)
return x + res
data = ...
input = np.array(
keras.preprocessing.sequence.pad_sequences(data["input"], padding="pre", dtype="float32"))
output = np.array(
keras.preprocessing.sequence.pad_sequences(data["output"], padding="pre", dtype="float32"))
# Input shape: (723, 36, 22)
# Output shape: (723, 36, 1)
# Train data
train_features = input[100:]
train_labels = output[100:]
train_labels = tf.keras.utils.to_categorical(train_labels, num_classes=3)
# Test data
test_features = input[:100]
test_labels = output[:100]
test_labels = tf.keras.utils.to_categorical(test_labels, num_classes=3)
inputs = keras.Input(shape=(None,22), dtype="float32", name="inputs")
# Ignore padding in inputs
x = layers.Masking(mask_value=0)(inputs)
x = transformer_encoder_block(x, head_size=64, num_heads=16, filters=3, dropout=0.2)
# Multiclass = Softmax (decrease, no change, increase)
outputs = layers.TimeDistributed(layers.Dense(3, activation="softmax", name="outputs"))(x)
# Create model
model = keras.Model(inputs=inputs, outputs=outputs)
# Compile model
model.compile(loss="categorical_crossentropy", optimizer=(tf.keras.optimizers.Adam(learning_rate=0.005)), metrics=['accuracy'])
# Train model
history = model.fit(train_features, train_labels, epochs=10, batch_size=32)
# Evaluate on the test data
test_loss = model.evaluate(test_features, test_labels, verbose=0)
print("Test loss:", test_loss)
out = model.predict(test_features)
After padding, input is of shape (723, 36, 22), and output is of shape (723, 36, 1) (before converting output to one hop, after which there are 3 output classes).
Here's an example output for ten epochs (trust me, more than ten doesn't make it better):
Epoch 1/10
20/20 [==============================] - 2s 62ms/step - loss: 10.7436 - accuracy: 0.3335
Epoch 2/10
20/20 [==============================] - 1s 62ms/step - loss: 10.7083 - accuracy: 0.3354
Epoch 3/10
20/20 [==============================] - 1s 60ms/step - loss: 10.6555 - accuracy: 0.3392
Epoch 4/10
20/20 [==============================] - 1s 62ms/step - loss: 10.7846 - accuracy: 0.3306
Epoch 5/10
20/20 [==============================] - 1s 60ms/step - loss: 10.7600 - accuracy: 0.3322
Epoch 6/10
20/20 [==============================] - 1s 59ms/step - loss: 10.7074 - accuracy: 0.3358
Epoch 7/10
20/20 [==============================] - 1s 59ms/step - loss: 10.6569 - accuracy: 0.3385
Epoch 8/10
20/20 [==============================] - 1s 60ms/step - loss: 10.7767 - accuracy: 0.3314
Epoch 9/10
20/20 [==============================] - 1s 61ms/step - loss: 10.7346 - accuracy: 0.3341
Epoch 10/10
20/20 [==============================] - 1s 62ms/step - loss: 10.7093 - accuracy: 0.3354
Test loss: [10.073813438415527, 0.375]
4/4 [==============================] - 0s 22ms/step
Using the same data on a simple LSTM model with the same shape yielded a desirable prediction with a constantly decreasing loss.
Tweaking the learning rate appears to have no effect, nor does stacking more transformer_encoder_block()s.
If anyone has any suggestions for how I can solve this, please let me know.
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I am training a CNN model with a 2D tensor of shape (400,22) as both input and output. I am using categorical_crossentropy both as loss and metric. However the loss/metrics values are very different.
My model is somewhat like this:
1. Using sample weights, and passing metrics with metrics= in model.compile.
# Imports
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.regularizers import *
from tensorflow.keras import *
import numpy as np
# Build the model
X_input = Input(shape=(400,22))
X = Conv1D(filters=32, kernel_size=2, activation='elu',
kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4),
padding='same')(X_input)
X = Dropout(0.2)(X)
X = Conv1D(filters=32, kernel_size=2, activation='elu',
kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4),
padding='same')(X)
X = Dropout(0.2)(X)
y = Conv1D(filters=22, kernel_size=1, activation='softmax',
kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4),
padding='same')(X)
model = Model(X_input, y, name='mymodel')
# Compile and train the model (with metrics=[])
model.compile(optimizer=Adam(1e-3),
loss=tf.keras.losses.categorical_crossentropy,
metrics=[tf.keras.losses.categorical_crossentropy])
Xtrain = np.random.rand(20,400,22)
ytrain = np.random.rand(20,400,22)
np.random.seed(0)
sample_weight = np.random.choice([0.01, 0.1, 1], size=20)
history = model.fit(x=Xtrain, y=ytrain, sample_weight=sample_weight, epochs=4)
Epoch 1/4
1/1 [==============================] - 0s 824us/step - loss: 10.2952 - categorical_crossentropy: 34.9296
Epoch 2/4
1/1 [==============================] - 0s 785us/step - loss: 10.2538 - categorical_crossentropy: 34.7858
Epoch 3/4
1/1 [==============================] - 0s 772us/step - loss: 10.2181 - categorical_crossentropy: 34.6719
Epoch 4/4
1/1 [==============================] - 0s 766us/step - loss: 10.1903 - categorical_crossentropy: 34.5797
From the results, it is evident that Keras is not using sample weights in the calculation of metrics, hence it is larger than the loss. If we change the sample weights to ones, we get the following:
2. Sample weights = ones, passing metrics with metrics= in `model.compile.
# Compile and train the model
model.compile(optimizer=Adam(1e-3),
loss=tf.keras.losses.categorical_crossentropy,
metrics=[tf.keras.losses.categorical_crossentropy])
Xtrain = np.random.rand(20,400,22)
ytrain = np.random.rand(20,400,22)
np.random.seed(0)
sample_weight = np.ones((20,))
history = model.fit(x=Xtrain, y=ytrain, sample_weight=sample_weight, epochs=4)
Epoch 1/4
1/1 [==============================] - 0s 789us/step - loss: 35.2659 - categorical_crossentropy: 35.2573
Epoch 2/4
1/1 [==============================] - 0s 792us/step - loss: 35.0647 - categorical_crossentropy: 35.0562
Epoch 3/4
1/1 [==============================] - 0s 778us/step - loss: 34.9301 - categorical_crossentropy: 34.9216
Epoch 4/4
1/1 [==============================] - 0s 736us/step - loss: 34.8076 - categorical_crossentropy: 34.7991
Now the metrics and loss are quite close with sample weights of ones. I understand that the loss is slightly larger than metrics due to the effects of dropout, regularization, and the fact that the metric is computed at the end of each epoch, whereas the loss is the average over the batches in the training.
How can I get the metrics to include the sample weights??
3. UPDATED: using sample weights, and passing metrics with weighted_metrics= in model.compile.
It was suggested that I used weighted_metrics=[...] instead of metrics=[...] in model.compile. However, Keras still does not include the sample weights in the evaluation of the metrics.
# Compile and train the model
model.compile(optimizer=Adam(1e-3),
loss=tf.keras.losses.categorical_crossentropy,
weighted_metrics=[tf.keras.losses.categorical_crossentropy])
Xtrain = np.random.rand(20,400,22)
ytrain = np.random.rand(20,400,22)
np.random.seed(0)
sample_weight = np.random.choice([0.01, 0.1, 1], size=20)
history = model.fit(x=Xtrain, y=ytrain, sample_weight=sample_weight, epochs=4)
Epoch 1/4
1/1 [==============================] - 0s 764us/step - loss: 10.2581 - categorical_crossentropy: 34.9224
Epoch 2/4
1/1 [==============================] - 0s 739us/step - loss: 10.2251 - categorical_crossentropy: 34.8100
Epoch 3/4
1/1 [==============================] - 0s 755us/step - loss: 10.1854 - categorical_crossentropy: 34.6747
Epoch 4/4
1/1 [==============================] - 0s 746us/step - loss: 10.1631 - categorical_crossentropy: 34.5990
What can be done to ensure that the sample weights are evaluated in the metrics?
Keras does not automatically include sample weights in the evaluation of metrics. That's why there is a huge difference between the loss and the metrics.
If you'll like to include sample weights when evaluating metrics, pass them as weighted_metrics rather than metrics.
model.compile(optimizer=Adam(1e-3),
loss=tf.keras.losses.categorical_crossentropy,
weighted_metrics=[tf.keras.losses.categorical_crossentropy]))
First of all, categorical cross-entropy is usually not used as a metric. Secondly, you are doing some type of seq2seq task, I hope you design the model with that intention.
Finally, in your setup, using sample_weight only works on the loss, it has no effect on the metrics or validation. There are other small bugs in your code too. Here is the fixed working code:
ref: TF 2.3.0 training keras model using tf dataset with sample weights does not apply to metrics (why sample_weight only works on loss)
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras import *
import numpy as np
X_input = Input(shape=(400,22))
X = Conv1D(filters=32, kernel_size=2, activation='elu', kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4), padding='same')(X_input)
X = Dropout(0.2)(X)
X = Conv1D(filters=32, kernel_size=2, activation='elu', kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4), padding='same')(X)
X = Dropout(0.2)(X)
y = Conv1D(filters=22, kernel_size=1, activation='softmax', kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4), padding='same')(X)
model = Model(X_input, y, name='mymodel')
model.compile(optimizer=Adam(1e-3), loss=tf.keras.losses.categorical_crossentropy,
metrics=[tf.keras.losses.categorical_crossentropy])
Xtrain = np.random.rand(10,400,22)
ytrain = np.random.rand(10,400,22)
history = model.fit(Xtrain, ytrain, sample_weight=np.ones(10), epochs=10)
Epoch 1/10
1/1 [==============================] - 1s 719ms/step - loss: 35.4521 - categorical_crossentropy: 35.4437
Epoch 2/10
1/1 [==============================] - 0s 20ms/step - loss: 35.5138 - categorical_crossentropy: 35.5054
Epoch 3/10
1/1 [==============================] - 0s 19ms/step - loss: 35.5984 - categorical_crossentropy: 35.5900
Epoch 4/10
1/1 [==============================] - 0s 19ms/step - loss: 35.6617 - categorical_crossentropy: 35.6533
Epoch 5/10
1/1 [==============================] - 0s 19ms/step - loss: 35.7807 - categorical_crossentropy: 35.7723
Epoch 6/10
1/1 [==============================] - 0s 19ms/step - loss: 35.9045 - categorical_crossentropy: 35.8961
Epoch 7/10
1/1 [==============================] - 0s 18ms/step - loss: 36.0590 - categorical_crossentropy: 36.0505
Epoch 8/10
1/1 [==============================] - 0s 19ms/step - loss: 36.2040 - categorical_crossentropy: 36.1956
Epoch 9/10
1/1 [==============================] - 0s 18ms/step - loss: 36.4169 - categorical_crossentropy: 36.4084
Epoch 10/10
1/1 [==============================] - 0s 32ms/step - loss: 36.6622 - categorical_crossentropy: 36.6538
Here, if you use no sample_weight or 1 for each sample, you will get close/similar categorical cross-entropy.
Use weighted_metrics according to docs.
I am trying to build an image classifier that differentiates images into pumps, Turbines, and PCB classes. I am using transfer learning from Inception V3.
Below is my code to initialize InceptionV3
import os
from tensorflow.keras import layers
from tensorflow.keras import Model
!wget --no-check-certificate \
https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 \
-O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
from tensorflow.keras.applications.inception_v3 import InceptionV3
local_weights_file = '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'
pre_trained_model = InceptionV3(input_shape = (150, 150, 3),
include_top = False,
weights = None)
pre_trained_model.load_weights(local_weights_file)
for layer in pre_trained_model.layers:
layer.trainable = False
# pre_trained_model.summary()
last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output
Next I connect my DNN to the pre-trained model:
from tensorflow.keras.optimizers import RMSprop
# Flatten the output layer to 1 dimension
x = layers.Flatten()(last_output)
# Add a fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dense(1024, activation='relu')(x)
# Add a dropout rate of 0.2
x = layers.Dropout(0.2)(x)
x = layers.Dense (3, activation='softmax')(x)
model = Model( pre_trained_model.input, x)
model.compile(optimizer = RMSprop(lr=0.0001),
loss = 'categorical_crossentropy',
metrics = ['accuracy'])
I feed in my images using ImageDataGenerator and train the model as below:
history = model.fit(
train_generator,
validation_data = validation_generator,
steps_per_epoch = 100,
epochs = 20,
validation_steps = 50,
verbose = 2)
However, the validation accuracy is not printed/generated after the first epoch:
Epoch 1/20
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0.
warnings.warn(str(msg))
/usr/local/lib/python3.6/dist-packages/PIL/Image.py:932: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
"Palette images with Transparency expressed in bytes should be "
WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 50 batches). You may need to use the repeat() function when building your dataset.
100/100 - 43s - loss: 0.1186 - accuracy: 0.9620 - val_loss: 11.7513 - val_accuracy: 0.3267
Epoch 2/20
100/100 - 41s - loss: 0.1299 - accuracy: 0.9630
Epoch 3/20
100/100 - 39s - loss: 0.0688 - accuracy: 0.9840
Epoch 4/20
100/100 - 39s - loss: 0.0826 - accuracy: 0.9785
Epoch 5/20
100/100 - 39s - loss: 0.0909 - accuracy: 0.9810
Epoch 6/20
100/100 - 39s - loss: 0.0523 - accuracy: 0.9845
Epoch 7/20
100/100 - 38s - loss: 0.0976 - accuracy: 0.9835
Epoch 8/20
100/100 - 39s - loss: 0.0802 - accuracy: 0.9795
Epoch 9/20
100/100 - 39s - loss: 0.0612 - accuracy: 0.9860
Epoch 10/20
100/100 - 40s - loss: 0.0729 - accuracy: 0.9825
Epoch 11/20
100/100 - 39s - loss: 0.0601 - accuracy: 0.9870
Epoch 12/20
100/100 - 39s - loss: 0.0976 - accuracy: 0.9840
Epoch 13/20
100/100 - 39s - loss: 0.0591 - accuracy: 0.9815
Epoch 14/20
I am not understanding as to what is stopping the validation accuracy from being printed/generated. I get an error if the plot a graph on accuracy vs validation accuracy with a message as:
ValueError: x and y must have same first dimension, but have shapes (20,) and (1,)
what am I missing here?
It worked finally, posting my changes here in case if anybody faces issues like these.
So I changed the "weights" parameter in InceptionV3 from None to 'imagenet' and calculated my steps per epoch and validations steps as follows:
steps_per_epoch = np.ceil(no_of_training_images/batch_size)
validation_steps = np.ceil(no_of validation_images/batch_size)
As you see WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least ``steps_per_epoch * epochs`` batches (in this case, 50 batches). You may need to use the repeat() function when building your dataset.
To make sure that you have "at least steps_per_epoch * epochs batches", set the steps_per_epoch to:
steps_per_epoch = X_train.shape[0]//batch_size
Well, i'm new to Machine Learning, and so with Keras. I'm trying to create a model from which can be passed as Input a list of arrays of arrays (a list of 6400 arrays within 2 arrays).
This is my code's problem:
XFIT = np.array([x_train, XX_train])
YFIT = np.array([y_train, yy_train])
Inputs = keras.layers.Input(shape=(6400, 2))
hidden1 = keras.layers.Dense(units=100, activation="sigmoid")(Inputs)
hidden2 = keras.layers.Dense(units=100, activation='relu')(hidden1)
predictions = keras.layers.Dense(units=3, activation='softmax')(hidden2)
model = keras.Model(inputs=Inputs, outputs=predictions)
There's no error; however, the Input layer (Inputs) forces me to pass a (6400, 2) shape, as each array (x_train and XX_train) has 6400 arrays inside. The result, with the epochs done, is this:
Train on 2 samples
Epoch 1/5
2/2 [==============================] - 1s 353ms/sample - loss: 1.1966 - accuracy: 0.2488
Epoch 2/5
2/2 [==============================] - 0s 9ms/sample - loss: 1.1303 - accuracy: 0.2544
Epoch 3/5
2/2 [==============================] - 0s 9ms/sample - loss: 1.0982 - accuracy: 0.3745
Epoch 4/5
2/2 [==============================] - 0s 9ms/sample - loss: 1.0854 - accuracy: 0.3745
Epoch 5/5
2/2 [==============================] - 0s 9ms/sample - loss: 1.0835 - accuracy: 0.3745
Process finished with exit code 0
I can't train more than twice in each epoch because of the input shape. How can I change this input?
I have triend other shapes but they got me errors.
x_train, XX_train seems like this
[[[0.505834 0.795461]
[0.843175 0.975741]
[0.22349 0.035036]
...
[0.884796 0.867509]
[0.396942 0.659936]
[0.873194 0.05454 ]]
[[0.95968 0.281957]
[0.137547 0.390005]
[0.635382 0.901555]
...
[0.887062 0.486206]
[0.49827 0.949123]
[0.034411 0.983711]]]
Thank you and forgive me if i've commited any fault, first time in Keras and first time in StackOverFlow :D
You are almost there. The problem is with:
XFIT = np.array([x_train, XX_train])
YFIT = np.array([y_train, yy_train])
Let's see with an example:
import numpy as np
x_train = np.random.random((6400, 2))
y_train = np.random.randint(2, size=(6400,1))
xx_train = np.array([x_train, x_train])
yy_train = np.array([y_train, y_train])
print(xx_train.shape)
(2, 6400, 2)
print(yy_train.shape)
(2, 6400, 1)
In the array, we have 2 batches with 6400 samples each. This means when we call model.fit, it only has 2 batches to train on. Instead, what we can do:
xx_train = np.vstack([x_train, x_train])
yy_train = np.vstack([y_train, y_train])
print(xx_train.shape)
(12800, 2)
print(yy_train.shape)
(12800, 1)
Now, we have correctly joined both sample and can now train.
Inputs = Input(shape=(2, ))
hidden1 = Dense(units=100, activation="sigmoid")(Inputs)
hidden2 = Dense(units=100, activation='relu')(hidden1)
predictions = Dense(units=1, activation='sigmoid')(hidden2)
model = Model([Inputs], outputs=predictions)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(xx_train, yy_train, batch_size=10, epochs=5)
Train on 12800 samples
Epoch 1/5
12800/12800 [==============================] - 3s 216us/sample - loss: 0.6978 - acc: 0.5047
Epoch 2/5
12800/12800 [==============================] - 2s 186us/sample - loss: 0.6952 - acc: 0.5018
Epoch 3/5
12800/12800 [==============================] - 3s 196us/sample - loss: 0.6942 - acc: 0.4962
Epoch 4/5
12800/12800 [==============================] - 3s 217us/sample - loss: 0.6938 - acc: 0.4898
Epoch 5/5
12800/12800 [==============================] - 3s 217us/sample - loss: 0.6933 - acc: 0.5002
I am training a simple machine learning model that takes a 1D description of a physical system (502 elements) and predicts the total energy (1 element). As I am new to TensorFlow I have used a simple dense neural network with two hidden layers of 64 neurons each:
Model: "total_energy"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
charge_density_x_max (InputL [(None, 502)] 0
_________________________________________________________________
hidden_1 (Dense) (None, 64) 32192
_________________________________________________________________
hidden_2 (Dense) (None, 64) 4160
_________________________________________________________________
dense (Dense) (None, 1) 65
=================================================================
Total params: 36,417
Trainable params: 36,417
Non-trainable params: 0
_________________________________________________________________
This is my source code for the training, evaluation and prediction:
# imports
import os
import ast
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
# load the dataset from the csv file
data = pd.read_csv('1e_data.csv')
# load in the data
x_train = np.zeros(shape=(600, 502))
x_test = np.zeros(shape=(400, 502))
y_train = np.zeros(shape=(600))
y_test = np.zeros(shape=(400))
for i in range(0, 1000):
if i < 600:
x_train[i,:] = np.append(np.array(ast.literal_eval(data.loc[i,'n'])), float(data.loc[i,'xmax']))
y_train[i] = float(data.loc[i,'E'])
else:
x_test[i-600,:] = np.append(np.array(ast.literal_eval(data.loc[i,'n'])), float(data.loc[i,'xmax']))
y_test[i-600] = float(data.loc[i,'E'])
# build the neural network model
inputs = tf.keras.Input(shape=(502,), name='charge_density_x_max')
hidden1 = tf.keras.layers.Dense(64, activation='sigmoid', name='hidden_1')(inputs)
hidden2 = tf.keras.layers.Dense(64, activation='sigmoid', name='hidden_2')(hidden1)
outputs = tf.keras.layers.Dense(1)(hidden2)
model = tf.keras.Model(inputs=inputs, outputs=outputs, name='total_energy')
# save the info of the model
with open('model_info.dat','w') as fh:
model.summary(print_fn=lambda x: fh.write(x + '\n'))
# compile the model
model.compile(optimizer='adam', loss='mean_absolute_percentage_error', metrics=['accuracy'])
# perform the training
model.fit(x_train, y_train, epochs=10)
# evaluate the model for accuracy
model.evaluate(x_test, y_test, verbose=2)
Yet when I run this it seems to do no training at all, giving an accuracy of 0.0000e+00:
Epoch 1/10
600/600 [==============================] - 0s 196us/sample - loss: 289.0616 - acc: 0.0000e+00
Epoch 2/10
600/600 [==============================] - 0s 37us/sample - loss: 144.5967 - acc: 0.0000e+00
Epoch 3/10
600/600 [==============================] - 0s 46us/sample - loss: 97.2109 - acc: 0.0000e+00
Epoch 4/10
600/600 [==============================] - 0s 46us/sample - loss: 108.0698 - acc: 0.0000e+00
Epoch 5/10
600/600 [==============================] - 0s 47us/sample - loss: 84.5921 - acc: 0.0000e+00
Epoch 6/10
600/600 [==============================] - 0s 38us/sample - loss: 79.9309 - acc: 0.0000e+00
Epoch 7/10
600/600 [==============================] - 0s 38us/sample - loss: 80.6755 - acc: 0.0000e+00
Epoch 8/10
600/600 [==============================] - 0s 47us/sample - loss: 87.5954 - acc: 0.0000e+00
Epoch 9/10
600/600 [==============================] - 0s 46us/sample - loss: 73.6634 - acc: 0.0000e+00
Epoch 10/10
600/600 [==============================] - 0s 38us/sample - loss: 78.0825 - acc: 0.0000e+00
400/400 - 0s - loss: 70.3813 - acc: 0.0000e+00
I have probably made a simple mistake here, but I do not know how to begin debugging. This should perform at least some training, but at the moment it seems to just skip the training and give an accuracy of 0.
You are in a regression setting, where accuracy is meaningless (it is meaningful only for classification problems); see What function defines accuracy in Keras when the loss is mean squared error (MSE)? for more details (it is applicable in your case, too, despite the use of a different loss).
The fact that your network does indeed learn is apparent from the reduction in your loss, which is the actual quantity of interest in regression problems (you simply don't need any metrics here).
Independently of the above, you should probably change the sigmoid activations to relu (we normally do not use sigmoid nowadays for intermediate layers).