CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasdeep-learning
ChemBot
ChemBot

Reputation: 77

Why does Keras model.summary() not work for my model?

I have written the following Keras §model:

input = Input(shape=(train.shape[1:]))


x = layers.Conv1D(filters=32, padding="valid", activation="relu", strides=1, kernel_size=1)(input)
x = layers.Conv1D(filters=32, padding="valid", activation="relu", strides=1, kernel_size=1)(x)
x = layers.Conv1D(filters=32, padding="valid", activation="relu", strides=1, kernel_size=1)(x)
x = layers.GlobalMaxPooling1D()(x)



x = layers.Dense(1024, activation="relu")(x)
x = layers.Dropout(0.1)(x)
x = layers.Dense(1024, activation='relu')(x)
x = layers.Dropout(0.1)(x)


predictions = layers.Dense(1,kernel_initializer='normal')(x)

model = tf.keras.Model(inputs=[protein_input], outputs=[predictions])
plot_model(model,"model.png", show_dtype=True, show_shapes=True, show_layer_names=True)

model.summary()

Which produces this table:

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
Total params: 1,086,593
Trainable params: 1,086,593
Non-trainable params: 0
_________________________________________________________________

plot_model() also does not produce a diagram.

The model compiles, and I can run model.fit.

model.compile(loss=tf.keras.losses.MeanSquaredError(), optimizer="adam", metrics=['mean_absolute_error'])

epochs=100

history = model.fit(x=[train],y=log_training_Kd_labels,
                                validation_data=([val],log_validation_Kd_labels),
                                epochs=epochs,
                                batch_size=100)

But I am not sure if any learning is occurring. Can anyone advise why this is happening?

Upvotes: 0

Views: 2469

Answers (1)

Kaveh
Kaveh

Reputation: 4960

You are probably mixing keras and tensorflow libraries. Since Tensorflow implemented keras libraries, this a common mistake between developers that import keras and tensorflow and use both of them randomly and this leads to some weird behavior.

Just use either import tensorflow.keras or import keras in your entire code.

For example if I code like this (use both library randomly):

import keras                                #import keras
import tensorflow as tf
from tensorflow.keras.layers import Dense   #import layers from tensorflow.keras
from tensorflow.keras import Input

input = Input(shape = (20,))
x = Dense(30, name = 'dense1')(input)
x = Dense(20, name = 'dense2')(x)
output = Dense(1)(x)
model = keras.models.Model(inputs = input ,outputs = output)
model.compile(loss = 'mse', optimizer = 'adam')
model.summary()

The output will be:

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
Total params: 1,271
Trainable params: 1,271
Non-trainable params: 0
_________________________________________________________________

But if I modify imports and use just tensorflow.keras and not use keras like this:

import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras import Input
from tensorflow.keras.models import Model

input = Input(shape = (20,))
x = Dense(30, name = 'dense1')(input)
x = Dense(20, name = 'dense2')(x)
output = Dense(1)(x)
model = Model(inputs = input ,outputs = output)
model.compile(loss = 'mse', optimizer = 'adam')
model.summary()

I will get the output like this:

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         [(None, 20)]              0         
_________________________________________________________________
dense1 (Dense)               (None, 30)                630       
_________________________________________________________________
dense2 (Dense)               (None, 20)                620       
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 21        
=================================================================
Total params: 1,271
Trainable params: 1,271
Non-trainable params: 0
_________________________________________________________________

Upvotes: 1

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