import tensorflow.keras as keras
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
model = keras.models.load_model('model/model_test_0.99408.h5', custom_objects={'leaky_relu': tf.nn.leaky_relu})
model.summary()
inputs = keras.layers.Input(shape=(28, 28, 1))
y = model(inputs)
feature = model.get_layer('conv2d_4').output
model = keras.Model(inputs=inputs, outputs=[y, feature])
model.summary()
why i can't get the output of 'conv2d_4' that is the internal layer of the model? And i get the following error.
Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(None, 28, 28, 1), dtype=float32) at layer "conv2d". The following previous layers were accessed without issue: []
We can try restacking the model, assigning feature to the required layer,
import tensorflow.keras as keras
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
model = keras.models.load_model('model/model_test_0.99408.h5', custom_objects={'leaky_relu': tf.nn.leaky_relu})
model.summary()
inputs = keras.layers.Input(shape=(28, 28, 1))
y = inputs
for layer in vgg.layers:
if layer.name == 'conv2d_4':
feature = y
y = layer( y )
model = keras.Model(inputs=inputs, outputs=[y, feature])
model.summary()
Related
I am new to Python and facing few issues while implementing Neural Networks on a Earthquake prediction problem.
There is very rare material availabale online to solve this issue using neural networks, so got struck.
Please support.
Model.predict throwing TypeError: 'numpy.ndarray' object is not callable.
enter link description here
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import tensorflow as tf
train_data = pd.read_csv("C:\\Users\\rjraj\\Desktop\\mma\\ML & AI\\Project\\train_values.csv")
train_labels = pd.read_csv("C:\\Users\\rjraj\\Desktop\mma\\ML & AI\\Project\\train_labels.csv")
test_labels = pd.read_csv("C:\\Users\\rjraj\\Desktop\\mma\\ML & AI\\Project\\test_values.csv")
X_tr = train_data
X_te = test_labels
y_tr = train_labels['damage_grade'].values
# label encoding the categorical variables
label_encoding_columns=['land_surface_condition', 'foundation_type', 'roof_type',
'ground_floor_type', 'other_floor_type', 'position',
'plan_configuration', 'legal_ownership_status']
# label encoding categorical columns in train dataset
for i in label_encoding_columns:
X_tr[i]=X_tr[i].astype("category")
X_tr[i]=X_tr[i].cat.codes
# label encoding categorical columns in test dataset
for j in label_encoding_columns:
X_te[j]=X_te[j].astype("category")
X_te[j]=X_te[j].cat.codes
from sklearn.model_selection import train_test_split
X_train, X_test,y_train, y_test = train_test_split(X_tr,y_tr,test_size = 0.3,random_state = 42)
X_train.shape
(182420, 39)
X_test.shape
(78181, 39)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(X_train)
MinMaxScaler()
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
#from tensorflow.keras.optimizers import Adam
model = Sequential()
model.add(Dense(4, activation = 'relu'))
model.add(Dense(4, activation = 'relu'))
model.add(Dense(1))
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
model.fit(x=X_train, y=y_train, epochs=30)
model.evaluate(X_test,y_test, verbose = 0)
model.evaluate(X_train,y_train, verbose = 0)
test_pred = model.predict(X_test)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-74-82e9029ecb43> in <module>
----> 1 test_pred = model.predict(X_test)
TypeError: 'numpy.ndarray' object is not callable
i have a error in following code , error in 2st part on code and on first part i am declaring my dataset , layers etc.
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data=pd.read_excel('/content/dataset.xlsx')
data.head()
data.plot(kind='scatter', x='fiyat', y='yil',alpha = 0.5,color = 'red')
plt.xlabel('price') # label = name of label
plt.ylabel('year')
plt.title('Fiyat ve yil Scatter Plot')
data.plot(kind='scatter', x='fiyat', y='km',alpha = 0.5,color = 'grey')
plt.xlabel('price') # label = name of label
plt.ylabel('km')
plt.title('Fiyat ve km Scatter Plot')
data.plot(kind='scatter', x='fiyat', y='motor_gucu_hp',alpha = 0.5,color = 'green')
plt.xlabel('price') # label = name of label
plt.ylabel('machine power')
plt.title('fiyat ve motor_gucu_hp Scatter Plot')
# Importing the dataset
X = data.iloc[:, data.columns != 'fiyat']
y = data.fiyat
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.preprocessing import StandardScaler
from matplotlib import pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split
# define base model
def baseline_model():
# create model
model = Sequential()
model.add(Dense(30, input_dim=120, kernel_initializer='normal', activation='relu'))
model.add(Dense(120, activation = 'relu'))
model.add(Dense(120, activation = 'relu'))
model.add(Dense(1, kernel_initializer='normal'))
# Compile model
model.compile(loss='mse',
optimizer='adam',
metrics=['mae'] )
return model
model = baseline_model()
model.summary()
And getting error in here ; on model.fit location
import tensorflow as tf
from tensorflow import keras
import numpy as np
# Display training progress by printing a single dot for each completed epoch
EPOCHS = 500
# Store training stats
history = model.fit(X_train, y_train, epochs=EPOCHS,
batch_size=16, verbose=0)
ValueError: Input 0 of layer sequential_2 is incompatible with the layer: expected axis -1 of input shape to have value 120 but received input with shape (None, 47)
And error like this , can you help? What can i do.
So, I have been trying to apply LSTM on this csv file CSV File that im trying to train
However, it seems to train it self but after the training, its causing issue on my test file with either
Error 1
Or if I modify it a little pit then I get another error which says "Value Error: cannot reshape array of size 1047835 into shape"
Here is the code im implementing:-
import math
import matplotlib.pyplot as plt
import keras
import pandas as pd
import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" #Had to use CPU because of gpus capability was 3.0
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import *
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from keras.callbacks import EarlyStopping
df=pd.read_csv(r'C:\Users\LambertThePrick\Desktop\Databysir\LSTM.csv')
print(df.shape)
print(df.head(5))
#df.head(5)
TrainPart=df.iloc[:800,1:3].values
test_set=df.iloc[800:,1:3].values
scaler=MinMaxScaler(feature_range=(0,1))
Trainpart_scaled=scaler.fit_transform(TrainPart)
print(Trainpart_scaled)
X_Train=[]
Y_Train=[]
for i in range(60,800):
X_Train.append(Trainpart_scaled[i-60:i,0])
Y_Train.append(Trainpart_scaled[i,0])
X_Train,Y_Train=np.array(X_Train),np.array(Y_Train)
X_Train = np.reshape(X_Train, (X_Train.shape[0], X_Train.shape[1], 1))
# print(X_train = np.reshape(X_Train, (X_Train.shape[0], X_Train.shape[1], 1)))
#(740, 60, 1)
model = Sequential()
#Adding the first LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50, return_sequences = True, input_shape = (X_Train.shape[1], 1)))
model.add(Dropout(0.2))
# Adding a second LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50, return_sequences = True))
model.add(Dropout(0.2))
# Adding a third LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50, return_sequences = True))
model.add(Dropout(0.2))
# Adding a fourth LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50))
model.add(Dropout(0.2))
# Adding the output layer
model.add(Dense(units = 1))
# Compiling the RNN
model.compile(optimizer = 'adam', loss = 'mean_squared_error')
# Fitting the RNN to the Training set
model.fit(X_Train, Y_Train, epochs = 100, batch_size = 32)
#THIS IS EXPT AFTER THIS
dataset_train = df.iloc[:800, 1:3]
dataset_test = df.iloc[800:, 1:3]
dataset_total = pd.concat((dataset_train, dataset_test), axis = 0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1,1)
inputs = scaler.transform(inputs)
X_Test = []
for i in range(60, 800):
X_Test.append(inputs[i-60:i, 0])
X_Test = np.array(X_Test)
X_Test = np.reshape(X_Test, (X_Test.shape[0], X_Test.shape[1], 1))
print(X_Test.shape)
predicted_stock_price = model.predict(X_Test)
predicted_stock_price = scaler.inverse_transform(predicted_stock_price)
plt.plot(df.loc[800:, 'Date'],dataset_test.values, color = 'red', label = 'Real ASTL Stock Price')
plt.plot(df.loc[800:, 'Date'],predicted_stock_price, color = 'blue', label = 'Predicted ASTL Stock Price')
plt.xticks(np.arange(0,459,50))
plt.title('ASTL Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('ASTL Stock Price')
plt.legend()
plt.show()
You have a moment in your reshaping where you end up with a non-integer division. Take this example:
import numpy as np
data = np.zeros(3936)
out = data.reshape((-1,1,24,2))
works well because 3936/24/2 results in an integer, 82 .
But in this example
import numpy as np
data = np.zeros(34345)
out = data.reshape((-1,1,24,2))
you end up with the error message ValueError: cannot reshape array of size 34345 into shape (1,24,2) because the division does not result in an integer.
So, looping the way you do is bound to result in events of that type.
I have a simple CNN with inputs of shape (5,5,3). As a first step I want to add a constant tensor to the input.
With the code below, I get
AttributeError: 'NoneType' object has no attribute '_inbound_nodes'
I have tried a few things like
const_change = Input(tensor=tf.constant([ ...
or
const_change = Input(tensor=K.variable([ ...
but nothing seems to work. Any help is highly appreciated.
from __future__ import print_function
import tensorflow as tf
import numpy as np
import keras
from keras import backend as K
from keras.models import Model
from keras.layers import Input
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
# Python 2.7.10
# keras version 2.2.0
# tf.VERSION '1.8.0'
raw_input = Input(shape=(5, 5, 3))
const_change = tf.constant([
[[5.0,0.0,0.0],[0.0,0.0,-3.0],[-10.0,0.0,0.0],[0.0,0.0,4.0],[-20.0,0.0,0.0]],
[[-15.0,0.0,12.0],[0.0,4.0,0.0],[-3.0,0.0,10.0],[-18.0,0.0,0.0],[20.0,0.0,-6.0]],
[[0.0,0.0,6.0],[0.0,-2.0,-6.0],[0.0,0.0,2.0],[0.0,0.0,-9.0],[7.0,-6.0,0.0]],
[[-3.0,4.0,0.0],[11.0,-12.0,0.0],[0.0,0.0,0.0],[0.0,0.0,7.0],[0.0,0.0,2.0]],
[[0.0,0.0,0.0],[0.0,1.0,-2.0],[4.0,0.0,3.0],[0.0,0.0,0.0],[0.0,0.0,0.0]]])
cnn_layer1 = Conv2D(32, (4, 4), activation='relu')
cnn_layer2 = MaxPooling2D(pool_size=(2, 2))
cnn_layer3 = Dense(128, activation='relu')
cnn_layer4 = Dropout(0.1)
cnn_output = Dense(4, activation='softmax')
proc_input = keras.layers.Add()([raw_input, const_change])
# proc_input = keras.layers.add([raw_input, const_change]) -> leads to the same error (see below)
lay1 = cnn_layer1(proc_input)
lay2 = cnn_layer2(lay1)
lay3 = Flatten()(lay2)
lay4 = cnn_layer3(lay3)
lay5 = cnn_layer4(lay4)
lay_out = cnn_output(lay5)
model = Model(inputs=raw_input, outputs=lay_out)
# -> AttributeError: 'NoneType' object has no attribute '_inbound_nodes'
The const_change should be also Input just like raw_input. You can create another input layer named const_input, and feed raw_input and const_input together into model.
...
const_input = Input(tensor=const_change)
...
proc_input = keras.layers.Add()[raw_input, const_input]
...
model = Model(inputs=[raw_input, const_input], outputs=lay_out)
When I try to give Elmo embedding layer output to conv1d layer input it giving the error
ValueError: Input 0 is incompatible with layer conv1d_1: expected ndim=3, found ndim=2
I want to add a convolution layer from the output of the Elmo embedding layer
import tensorflow as tf
import tensorflow_hub as hub
import keras.backend as K
from keras import Model
from keras.layers import Input, Lambda, Conv1D, Flatten, Dense
from keras.utils import to_categorical
from sklearn.preprocessing import LabelEncoder
import pandas as pd
from sklearn.model_selection import train_test_split
df = pd.read_csv("/home/raju/Desktop/spam.csv", encoding='latin-1')
X = df['v2']
Y = df['v1']
le = LabelEncoder()
le.fit(Y)
Y = le.transform(Y)
Y = to_categorical(Y)
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)
elmo = hub.Module('/home/raju/models/elmo')
def embeddings(x):
return elmo(tf.squeeze(tf.cast(x, dtype=tf.string)), signature='default', as_dict=True)['default']
input_layer = Input(shape=(1,), dtype=tf.string)
embed_layer = Lambda(embeddings, output_shape=(1024,))(input_layer)
conv_layer = Conv1D(4, 2, activation='relu')(embed_layer)
fcc_layer = Flatten()(conv_layer)
output_layer = Dense(2, activation='softmax')(fcc_layer)
model = Model(inputs=[input_layer], outputs=output_layer)
A Conv1D layer expects input of the shape (batch, steps, channels). The channels dimension is missing in your case, and you need to include it even if it is equal to 1. So the output shape of your elmo module should be (1024, 1) (this does not include the batch size). You can add a dimension to the output of the elmo module with tf.expand_dims(x, axis=-1).